THE ISBA BULLETIN

Vol. 17 No. 3 September 2010

The o�cial bulletin of the International Society for Bayesian Analysis

A MESSAGE FROM THEPRESIDENT

Peter MullerISBA President, 2010

pm@odin.mdacc.tmc.edu

First some sad news. In August we lost twobig Bayesians. Julian Besag passed away on Au-gust 6, and Arnold Zellner passed away on Au-gust 11. Arnold was one of the founding ISBApresidents and was instrumental to get BayesianAnalysis started. Obituaries on this issue of theBulletin and on our homepage acknowledge Juli-an and Arnold’s pathbreaking contributions andtheir impact on the lives of many people in ourresearch community. They will be missed dearly!

ISBA Elections 2010. Please check out the elec-tion statements of the candidates for the upco-ming ISBA elections. We have an amazing slateof candidates. Thanks to the nominating commit-tee, Mike West (chair), Renato Martins Assuncao,Jennifer Hill, Beatrix Jones, Jaeyong Lee, Yasuhi-ro Omori and Gareth Robert!

Sections. ISBA now has a Objective BayesianSection (ISBA/OB). The new section was enthu-siastically approved by the board. The section of-ficers are Jim Berger (chair), Dongchu Sun (pro-gram chair), Jaeyong Lee (secretary) and Bru-nero Liseo (treasurer). Thanks to the foundingmembers for taking the initiative to launch thisnew section. The long running series of bienni-al O’Bayes workshops is one of the most excitingregular Bayesian meetings. It was embarrassingthat this happened without ISBA. Thanks to thefounding members of the new section to bringthis active area of Bayesian research into ISBA.We welcome the new section and invite membersto join by simply checking a box in the annual

membership renewal (there will be special provi-sions for members who hold multi-year member-ships).

The Bayesian Nonparametrics Section (IS-BA/BNP) is already up and running, and plan-ning the 2011 BNP workshop. Please see theNews from the World section in this Bulletinand our homepage (select “business” and “mee-tings”).

ISBA/SBSS Educational Initiative. Jointlywith ASA/SBSS (American Statistical Associa-tion, Section on Bayesian Statistical Science) welaunched a new joint educational initiative. Theinitiative formalized a long standing history ofcollaboration of ISBA and ASA/SBSS in relatedmatters. . . Continued on page 2.

In this issue‰ A MESSAGE FROM THE BA EDITOR

*Page 2

‰ 2010 ISBA ELECTION*Page 3

‰ SAVAGE AWARD AND MITCHELL PRIZE 2010*Page 8

‰ ARNOLD ZELLNER 1927-2010*Page 9

‰ JULIAN BESAG 1945-2010*Page 10

‰ ANNOTATED BIBLIOGRAPHY*Page 12

‰ SOFTWARE HIGHLIGHT*Page 15

‰ STUDENTS’ CORNER*Page 17

‰ NEWS FROM THE WORLD

*Page 18

ISBA Bulletin, 17(3), September 2010 BAYESIAN ANALYSIS - A MESSAGE FROM THE EDITOR

MESSAGE FROM THE PRESIDENT, Continuedfrom page 1. . . .

We hope to eventually jointly sponsor shortcourses and other activities in keeping with ourshared mission of promoting Bayesian analysis.We would welcome any other interested profes-sional societies, including in particular other na-tional societies to join the initiative. Marina Van-nucci (SBSS) and Peter Hoff (ISBA) have kindlyagreed to serve as representatives of the two so-cieties to coordinate any upcoming activities.

Savage Prize and Mitchell Prize Nominations:One of the most visible ISBA initiatives is theSavage Prize, awareded every year for the bestBayesian dissertations. Please consider nomina-ting outstanding recent Ph.D. theses! Any ISBAmember can submit nominations. The perhapsbiggest award is the invitation of all finalists topresent their work in a super prominent sessionat the upcoming JSM meeting. The Mitchell Pri-ze is awarded for outstanding applied Bayesianpapers. Please consider nominating eligible pa-pers that you enjoyed reading! Please see the no-te elsewhere in this Bulletin and our homepagewww.bayesian.org (click Prizes”) for details.s

A MESSAGE FROM THE EDITOR

Manuel Mendozamendoza@itam.mx

During the last quarter we all have witnessedhow the Bayesian Universe has continued its ex-pansion. Our regular activities have been gro-wing; new initiatives were announced and thefuture appears full of exciting challenges. It mustrecognized however, that some sad news have al-so arrived with the end of the summer.

As our President has mentioned above, Prof.Arnold Zellner passed away on August 10. Fourdays before, on August 6, Prof. Julian Besag alsopassed away. This issue includes a note on eachone of these distinguished colleagues. We are de-eply thankful to Prof. Siddharta Chib, Prof. JohnGeweke and Prof. Peter Green for these contri-

butions. Other regretable losses are those of thealways inspiring Prof. David Blackwell who pas-sed away on July 8 and Prof. John Nelder whopassed away on August 7. An obituary for Prof.Blackwell, by Robert Sanders, can be found at theUC Berkeley website. As for Prof. Nelder, a beau-tiful obituary by Prof. David Hand can be foundat Imperial College’s website

In any case, life continues and this issue inclu-des most of the usual sections. I might call yourattention to the information regarding the forth-coming ISBA elections. We have an impressivelist of nominees and this material will help youto make your decision.

As always, I want to encourage all members ofISBA to contribute to the Bulletin with their sug-gestions, manuscripts and announcements. Plea-se do not hesitate to contact me or any memberof the Editorial Board.s

BAYESIAN ANALYSIS - A MESSAGE FROM THE EDITOR

UPDATE FROM BAHerbie Lee

Editor-in-Chief

herbie@ams.ucsc.edu

The September issue of Bayesian Analysis fea-tures a discussion paper on the practical Bayesian

analysis of large datasets using mixture models,by Ioanna Manolopoulou, Cliburn Chan, and Mi-ke West. Discussions by Fabio Rigat and NickWhiteley provide some additional ideas and di-rections for thought. The remainder of the issuecontains six other fine articles in areas of Bayesi-an computation and modeling, and an applicati-on to hydrology.s

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2010 ISBA ELECTION

Candidate StatementsMerlise Clyde

clyde@stat.duke.edu

The 2010 ISBA nominations committee, chai-red by past-President Mike West, has assemb-led an outstanding slate of candidates for Pre-sident Elect, Treasurer, and four open Board ofDirector positions. Biographical information andcandidate statements appear below, as well ason the ISBA website. ISBA will open electronicvoting via the ISBA website beginning October15th, with elections closing November 15th. Allcurrent members will be emailed instructions forvoting prior to the election.

President 2012 (President Elect 2011, Past Pre-sident 2013)

Fabrizio Ruggeri (CNR-IMATI Milan, Italy)

I would like to thank the Nominations Com-mittee for inviting me to run, for the third ti-me, for ISBA President. I am very flattered sincethree independent committees, whose membersare deeply involved in ISBA activities, thought ofme for such a prestigious job. Now I have just toconvince ISBA members I am fit for it...

I believe ISBA has the mission to promo-te sound statistical methods and applications,worldwide and across disciplines, in the socie-ty, in teaching and in the research. Here are myplans to contribute to it, if elected, and some in-formation on me.

I am an ISBA member since the first meeting inSan Francisco in 1993 and I served ISBA in manyways: Board member, member of three Nomina-tions Committee, four times member of the Sa-vage Prize Selection Committee (and chair of thelatest one), once member of the De Groot PrizeSelection Committee, Chair of ISBA2004 Scienti-fic Committee, founding (and current) Editor ofBayesian Analysis and its first Production Editor,Editor of ISBA Bulletin (and responsible for itstransformation into its current format), one of themain proposers of the Pilar Iglesias Fund, Chairof the recent Editors Search Committee and Chairof ISBA endorsed workshops (on robustness, the

first on nonparametrics and the BISP series onstochastic processes).

The President-Elect will be in charge of IS-BA2012 in Japan: it is a great opportunity to ex-pand ISBA activities well beyond its stronghold(North America, Western Europe and Australia).Our focus should be in promoting local chap-ters, regional workshops, cooperation with lo-cal statistical societies, courses by leading Baye-sians, joint research programmes (e.g. looking foropportunities offered by the European Union)in Asia, Africa, Eastern Europe and Central andSouth America (without forgetting our strong-hold...). Those activities should have continuityin time, being, possibly, coordinated by an ad hoccommittee. Although deeply rooted in my home-lands (Italy and European Union), where I amResearch Director at the Italian National Rese-arch Council and Faculty in a Ph.D. programmein Pavia, I am a typical example of globalisati-on: I got my M.Sc. at Carnegie Mellon and myPh.D. at Duke in the USA, I am Adjunct Facultyat New York University and, this fall, Visiting Fa-culty at George Washington University, besidesbeing Faculty in a Ph.D. programme in Algeriaand having given many courses in Italy, Spain,Brazil and Chile (even in Spanish). I am also ve-ry honoured of being an Advisory Editor of theChilean Journal of Statistics and having strongconnection with Latin America, besides coopera-tion in Eastern Europe and a recent one with Tai-wan. Finally, I am Fellow of the American Stati-stical Association.

ISBA should continue its policy towards agreater cooperation with scientists in other fields,through contacts with their organisations, inter-disciplinary workshops, invited papers in Baye-sian Analysis, groups within ISBA, plenary talksand courses. It is not only an issue of outreachbut also of continuity of the cooperation overtime. I am devoting many efforts in spreadingsound Bayesian methods especially among engi-neers and, more recently, biologists, epidemiolo-gists and MDs. My research interests go from ro-bustness, nonparametrics, wavelets and stocha-stic processes to reliability, project management,and, more recently, biostatistics and biology. Ihave been ENBIS (European Industrial Statisti-

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cians) President, I was Editor-in-Chief of Ency-clopedia of Statistics in Quality and Reliability(a major reference in the field) and I am Editor-in-Chief of Applied Stochastic Models in Busi-ness and Industry. I edited two books on Bayesi-an Robustness and two forthcoming books are onBayesian Inference in Stochastic Processes (as co-author) and Statistics in Healthcare Practice (asco-editor).

Young researchers are a top priority for ISBA.We should think of some form of active participa-tion in ISBA activities, besides offering courses,awards and travel grants (and expanding themas much as possible). We should discuss anddecide about forms of deeper involvement byyoung researchers: an autonomous group, parti-cipation in some ISBA committees, organisationof some events devoted to themselves? As Edi-tor of the ISBA Bulletin, I created the Students’Corner, run by students to promote their fellows’works. As Chair of ISBA2004 Scientific Commit-tee, I worked hard with Pilar Iglesias to bring ma-ny young people to Chile and, later, I was one ofthe promoters of the fund named after her to sup-port travel grants for students.

Last but not least, ISBA has its well establis-hed activities, including worldwide conferences,chapters and groups, Bayesian Analysis, Bulle-tin, and awards. They have been run very well sofar and they strongly depend on ISBA finances,so wealthy because of a wise administration inthe past. Continuation and improvement of suchsuccessful scientific and financial managementare the primary goals of the Board and, first ofall, of the President. New ideas are very welco-me and I will invite and strongly rely on inputsfrom members, as I did when I reshaped the ISBANewsletter or I chaired the Editors Search Com-mittee.

Xiao-Li Meng (Harvard U, USA)

By 2012, ISBA will be leaving its teenage yearsbehind. By any measure, ISBA has had a fabulousteen experience. Just take a look at the society’swebsite, or attend any of its conferences. We doso well on so many fronts, and with so much funand energy, just like teenagers. The use of ”tee-nagers” is more than a metaphor here, becausethe future of ISBA, say in its second 20 years, de-pends literally on those who are teenagers now.Yet this is a group that we have essentially ne-ver reached out to — undergraduate-level intro-

ductory courses from a Bayesian perspective arethe exception rather than the rule. It is also rareto find sessions or even talks on teaching Bayesi-an statistics at the undergraduate level, either atteaching conferences (e.g., out of over 110 sessi-ons at the recent 8th International Conference onTeaching Statistics, I found only one session onteaching Bayesian statistics) or at Bayesian con-ferences (e.g., I do not recall any such talks oc-curring at the ISBA meetings I have attended).

If we believe that Bayesian thinking is the keyfor scientific quantitative reasoning, and that the21st century belongs to Bayesian statistics, whyare we then not teaching it first, literally, that is,when future scientists, policy makers, educators,etc. are first exposed to serious quantitative re-asoning? Two common responses have been (1)there is already too much material for introduc-tory level statistics courses and students are al-ready too busy, and (2) Bayesian statistics is verydifficult to teach at the introductory level.

For (1), I would argue that it is exactly becau-se the introductory courses are filled with mes-sages that are inconsistent with Bayesian reaso-ning that we, at the very least, need to provi-de students with an opportunity to be exposedto Bayesian thinking before their minds get ”toobusy” with materials we think they should learnless or not at all. For those of us who enjoy occa-sional DIY (do it yourself), we understand wellhow building from scratch often takes signifi-cantly less effort than trying to remodel an ”oldhouse.”

As for (2), some recent experiences have taughtme that it is entirely possible to teach Bayesianthinking even before formally introducing proba-bility. For the past two summers, I have taught”Vital Statistics for Medical and Life Sciences”,a study-abroad course that my ”happy team” (agroup of Harvard graduate students and I) hasput together. The very first lecture starts withthe question of what is the chance of a wo-man with positive screening test result actual-ly having breast cancer, given the knowledge ofthe breast cancer prevalence and the specifici-ty and sensitivity of the screening test. Every-thing was described in terms of simple percen-tages and illustrated with tree diagrams and Po-werPoint animations. All students are undergra-duates, and the vast majority of them have noprior exposure to statistics or probability. Yet bythe end of the first lecture, they have learned the

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thinking process of going from prior to posterior,as well as the power of such reasoning, especial-ly because of the unexpectedness of the answer.My best ”teaching moment” was when a studentcame to me after the class and said ”This is justweird,” referring to the fact that the answer see-med to be substantially too small compared to his”prior intuition”, yet he had to agree that it is theright answer because of the ”evidence” presentedin the lecture, especially in multiple ways. Whata great demonstration of Bayesian reasoning atwork!

One of the wittiest puns of Morris DeGroot is”Bayesians know where the bar is” (if you don’tget the pun, email Ed George). We need manymore teenagers who know the right bar befo-re they reach legal drinking age. Certainly, the-re is no society more fitting than ISBA to leadthe collective and organized effort international-ly in providing Bayesian statistics education atthe undergraduate level. Efforts here range fromorganizing pedagogically passionate and experi-enced scholars to brainstorm and design effectiveteaching and learning strategies, to collecting in-formation on the problems and challenges facedat different institutions across different countries,and to providing a variety of sample curricula,teaching materials, and teacher training tailoredcorrespondingly. If I am given the honor to serveas ISBA President, I will give the highest priori-ty to fostering such endeavors and to integratingthem into ISBA’s on-going effort in building andenhancing the future force of the Bayesian armyto meet the ever-increasing need for principledquantitative reasoning in every walk of life.

Treasurer 2011-2013

Hedibert Lopes (U of Chicago, USA)

Honor was (and still is) my feeling when Ibrowsing through the list of past ISBA Treasu-rers. Bayesian statistics has done many things forme and I am privileged to be nominated to onemore of ISBA’s offices. I have been a member ofthe Savage Thesis Award for a couple of years,ISBA bulletin editor for another few years andcurrently serve as board member, which includedthe chair of the ad-hoc membership committeefor one year. I have also helped the foundationof ISBRA, the 10-year old Brazilian Chapter of IS-BA.

I have no prior experience as Treasurer, but I

certainly have highly qualified friends and col-leagues here at Chicago Booth. One such friend(and, unfortunately, former colleague) is CarlosCarvalho. I am also counting on my friends andformer Treasurers, whose expertise and experi-ence I will constantly and humbly seek.

I am currently Associate Professor of Econo-metrics and Statistics at the University of Chi-cago Booth School of Business. My research in-terests concentrate on Bayesian modeling andcomputation, with particular emphasis on lon-gitudinal, spatial and dynamic factor analysis,time-varying covariance models, dynamic mo-dels, Markov chain Monte Carlo and sequentialMonte Carlo methods. I published the book onMCMC for Bayesian inference with Dani Gamer-man (Chapman & Hall/CRC Press) and will pu-blish in 2012 a book on Bayesian Econometricswith Robert McCulloch (Wiley). My research pa-pers have been published in Statistical Science,Biometrics, Statistica Sinica, Journal of Time Se-ries Analysis, Statistics and Computing, Jour-nal of Statistical Planning and Inference, Com-putational Statistics and Data Analysis, to na-me just a few. I advise or advised around 20MSc and PhD students since 2000. I am currentlyAssociate Editor for Bayesian Analysis. Additio-nal information can be found on my website athttp://faculty.chicagobooth.edu/hedibert.lopes.

Mike Daniels (U of Florida, USA) I am very hap-py to be nominated for the Treasurer of ISBA. Ihave served ISBA in a variety of ways over theyears: Member of the Savage Thesis Award Com-mittee in 2007-2008 and 2008-2009 and Memberof the Mitchell Prize Committee in 2009-2010. Ialso served as the program chair for the Secti-on on Bayesian Statistical Science of the ASA in2009.

I also have relevant experience to serve as Tre-asurer given my holding the same position inENAR (of IBS) for 2010-2011 and in dealing withfinancial issues in my capacity as the chair of theDepartment of Statistics at the University of Flo-rida since 2008. I will use my experience to helpkeep ISBA in the best financial situation possible.

I am currently Professor and Chair in the De-partment of Statistics at the University of Flori-da. My research interests focus on Bayesian me-thodology for longitudinal data and missing da-ta and methodology for estimating dependence.I recently published a book (w/ Joe Hogan) on

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Bayesian methodology for missing data in lon-gitudinal studies (title: Missing Data in Longi-tudinal Studies: Strategies for Bayesian Mode-ling and Sensitivity Analysis, with Chapman &Hall/CRC Press). My research papers have be-en published in JASA, Biometrika, Biometrics,Biostatistics, JCGS, JMVA, among other venues.I am an Associate Editor for JASA, Biometrics,and Statistics & Probability Letters. I am current-ly working on collaborative projects in the areasof aging, genomics, infectious diseases (malariaand flu), weight management, and muscular dys-trophy. For more information on me, please visitmy website, http://www.stat.ufl.edu/ mdaniels.

Board of Directors 2011-2013 (4 openings, 8candidates listed randomly)

Dongchu Sun (U Missouri, USA)

Candidate Statement Pending, please see theISBA website for up-to-date information.

Ming-Hui Chen (U Connecticut, USA)

I am delighted to be a candidate for ISBA’sboard of directors. I am Professor and Directorof the Statistical Consulting Services in the De-partment of Statistics at University of Connec-ticut in Storrs, Connecticut, USA. Over last 20years, I have been working on methodologicaland application aspects of Bayesian statistics, in-cluding the Bayesian analysis of categorical da-ta, survival data, missing data, and genomic da-ta, the development of Monte Carlo methods forBayesian computations, and the Bayesian designof clinical trials. I have taught short courses onBayesian computations, Bayesian Survival ana-lysis, and missing data analysis at various mee-tings and universities including JSM’s, ENAR’s,WNAR, and ICSA symposiums. I have served asan Associate Editor of Bayesian Analysis since2004. I was the program chair of the Section onBayesian Statistical Sciences (SBSS) in 2005 andthe Publications Officer of SBSS for 2008-2009. Ialso served on the Board of Directors of the In-ternational Chinese Statistical Association (ICSA)(2004-2006). Currently, I am the Executive Direc-tor of ICSA (2007-2010). In 2009, ISBA and ICSAsigned the Memorandum of Understanding onthe co-sponsorship of future ISBA or ICSA mee-tings. In March 2010, I also served as the Associa-te Chair of the executive committee for the confe-rence titled ”Frontiers of Statistical Decision Ma-king and Bayesian Analysis”. If elected, my wor-

king experience being officer of SBSS and ICSAwould help increase the membership in the socie-ty and promote the collaboration between ISBAand other societies, and my research experienceas a Bayesian statistician would help promote thevalue and use of Bayesian statistics in interdisci-plinary research.

Chris Holmes (U Oxford, UK)

I am professor of Biostatistics at the Universi-ty of Oxford. My research interests surround ap-plications and methods development for Baye-sian analysis of high-dimensional data sets ari-sing in genomic and genetic epidemiology. Thishas lead me to investigate Bayesian nonparame-tric methods, including a recent book with Hjort,Mueller and Walker, as well as recent work inBayesian computation on graphics cards. I’ve ac-ted as Associate Editor for The Annals of Stati-stics and The Annals of Applied Statistics. I havealso served on the last two Savage Award com-mittees. I have organized workshops and invi-ted sessions on Bayesian statistics and their ap-plications in genetics. I have published papers inthe mainstream statistics journals and also in ma-chine learning, bioinformatics and genetic epide-miology. I would be honoured to serve on the IS-BA board.

Judith Rousseau (U Paris Dauphine, France)

I am professor at Universite Paris Dauphineand at the ENSAE (Paris, France). My researchinterests include interractions between Bayesianand frequentist methods and understanding theimpact of using a prior with a more recent focuson nonparametric models; they include also mo-del choice and tests and in a more applied per-spective prior elicitation. I am currently associateeditor of the Australian and New-Zealand Jour-nal of Statistics and of the Annals of Statistics. Ihave also been a member of the prize commit-tee for the last two years and last year I have ac-ted as Chair of the prize committee. I have be-en a member of the scientific committee for diffe-rent ISBA meetings and have organized Bayesiansessions at meetings. I have published papers inmainstream statistical journals and in computa-tional journals. I would be honoured to serve onthe ISBA board.

Hajime Wago (Kyoto Sangyo U, Japan)

I am delighted to be a candidate for ISBA’s

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board of directors. I am currently Professor ofEconometrics and Statistics at Kyoto Sangyo Uni-versity in Kyoto, Japan. Over the past two deca-des and more I have been working on variousaspects of Bayesian analysis, including high fre-quency financial data and spatial criminal andbusiness data, computational aspects such asMCMC methods, particle filtering and simulati-on based inference, modelling for the state spacetime series analysis, and macroeconometric mo-delling (Dynamic Stochastic General Equilibri-um (DSGE) Model). I have organized 15 confe-rences and workshops on theory and applicationof Bayesian statistics. Recently, I have also publis-hed four MCMC related Bayesian books in Japa-nese to promote Bayesian ideas on useful empiri-cal analysis.

As a new era of globalization intensifies, Japanstatistical society will attempt to expand its acti-vities across national boundaries and to consoli-date friendship and partnership with statisticiansin other Asian societies.

Regarding my contribution for ISBA, I serveda member of the founding board (1992-94) and Iremember the intense discussion on whether ha-ving our constitution and publishing the journalof ISBA.

Bayesian method is getting more popular in Ja-pan and Asia, so it is the best time for us to holdthe next ISBA2012 meeting in Kyoto. I am hostingit as the chair of Japanese organizing committee.I am confident the ISBA meetings have given andwill give a stimulus occasion for local young re-searchers and students.

If elected, my experience would help increasethe membership in the Society, and help promo-te the value of Bayesian thinking in statistics andthe applied areas.

Antonietta Mira (U Insubria, Italy)

I am professor of statistics both at the U. ofInsubria (Italy, where the Bayesian approach iswell established) and at the U. of Lugano (Swit-zerland, where there are very few universitiesthat have Bayesian courses at graduate or un-dergraduate level: I am helping spread the voicearound). I have been on the scientific committeeof the joint MCMSki I, II and III (co-chair) andI am co-editor of Bayesian Analysis. And I takethis opportunity to invite you to Salt Lake City in

January 2011 and to submit your papers to BA!

Besides having a vision of where Bayesian sta-tistics should go (which I’m sure is shared by al-most everybody who is on this list) I would liketo share with you my thoughts on how to helpmove in the right directions. I want to be veryconcrete so here is a TODO list (I was tempted towrite a WISH list but, in name of concreteness...)

1. know thyself (gnothi seauton, nosce te ip-sum): use Bayesian statistics to make infe-rence and prediction on who we are as acommunity, what is it that we do well andless well, and what we should do to impro-ve. To be concrete: have a thematic compe-tition where people are encouraged to usetheir favorite new modeling and inferenti-al tool to analyze ”Bayesian data”, which isnot data that is coherent and consistent butit is data that Bayesian have generated (he-re are a few examples: feed to your favoritetext classifier the abstract books of Valenciameetings, study the time-space model of lo-cation of Bayesian conferences, study the ti-me series of ”Bayesian papers” in statisticaljournals ...)

2. divide and conquer (divide et impera):which are the areas where we are good andthe ones were we can improve? Divide: ha-ve small meetings like MCMSki, Bayesi-an nonparametrics, Prior elicitation, Clini-cal trials, Genomic and proteomic ... Con-quer: spread the voice around! Provide fi-nancial support for invited talk at thematic”non-Bayesian” conferences (JSM does notcount, but the annual meeting of seismicforecast: an area where (Bayesian) statisti-cians have little to say, would be the perfectexample)

3. do ’model criticism’ about ISBA activities:these days we get a ”costumer satisfaction”questionaire for pretty much everything wedo, what about doing one on ourselves? Af-ter all statisticians are ”our costumers” andwe offer them conferences, journals, bulle-tins, websites ... for all these activities wecould have an on-line survey to get a fee-ling of how we are doing and get inputson what and how we could improve (the-se surveys would provide genuine ”Baye-sian data” to be analyzed along the spirit ofpoint 1)

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4. make sure that when you google ISBA(especially in some languages like italian)the first entry is not the Illinois State Bar As-sociation!

In compiling the above TODO list I have triedto be ’nonparametric’ in the sense of thinkingabout ISBA taking on new things, not bound byhard constraints: this is my vision on where Baye-sian statistics should go and the spirit that shouldinform our decisions.

Helene Massam (York U, Canada)

I am a Full Professor in the Department of Ma-thematics and Statistics at York University in To-ronto, Canada. My research area is in graphicalmodels and Bayesian inference. My recent rese-arch has focused on the development of prior dis-tributions for the covariance parameter in Gaus-sian models and for loglinear parameters in hier-archical loglinear models as well as on the geo-metry of discrete hierarchical models. My workstends to be of a theoretical nature but alwayswith a view to improve methods widely used inapplications. I have published papers in the An-nals of Statistics, Biometrika, Bernoulli and othermainstream journals. I am an Associate Editor ofBayesian Analysis. I have been a member of theStatistics NSERC Selection Committee, the IMSFellows Selection Committee and the IMS Presi-dent Selection Committee. I am also a Fellow of

the IMS.

Rosangela Loschi (UF Minas Gerais, Brazil)

I am professor and head of the Department ofStatistics at Universidade Federal de Minas Ge-rais, Brazil. I received my Ph. D. in Statistics in1998 from the Universidade de Sao Paulo, Bra-zil. I am currently AE for the Chilean Journal ofStatistics. My research interests include, amongothers, clusters and change points identification,survival analysis, flexible models using exten-ded classes of asymmetric distributions. I havepublished papers in Statistical journals, such as,J. of Multivariate Analysis, Computational Stati-stics and Data Analysis, Lifetime Data Analysis,Biometrical J., Test, and correlated areas as Com-puters and Operations Research and European J.Operation Research. I have been involved withthe foundation of ISBRA - the Brazilian Chapterof ISBA - in 2000, and served as ISBRA executivesecretary twice (2000-2002, 2006-2008). I was oneof the organizers of the I Latin American Meetingon Bayesian Statistics in 2002, which was consi-dered as an ISBA Regional meeting, of three Bra-zilian Meetings on Bayesian Statistics and someother local meetings. I also served on two ISBAnominate committees, the last one in 2009. It is agreat honor to be nominated as a candidate forthe ISBA office. If elected, I hope, I can help topromote ISBA in South America and increase thenumber of membership in our society. s

SAVAGE AWARD AND MITCHELL PRIZE 2010

CALL FOR NOMINATIONS

Peter Mullerpm@odin.mdacc.tmc.edu

The Savage Award, named in honor of Leo-nard J. ”Jimmie” Savage, is bestowed each year totwo outstanding Bayesian Ph.D. theses. The Sa-vage Prize is one of our most visible activities.The prize is jointly administered by ISBA andthe ASA Section on Bayesian Statistical Science(SBSS).

Deadline is Friday, Oct 29. Advisors, de-partment chairs or any ISBA or SBSS membermay nominate a dissertation for the prize. Plea-se see http://bayesian.org/awards/Savage.htmlfor details.

The Mitchell Prize is named after Toby J. Mit-chell and was established by his friends and col-leagues following his death from leukemia in1993. The Mitchell Prize is awarded in recogni-tion of an outstanding paper that describes howa Bayesian analysis has solved an important ap-plied problem. The Prize is jointly sponsored bythe Section on Bayesian Statistical Science (SBSS)of the ASA, the International Society for Bayesi-an Analysis (ISBA), and the Mitchell Prize Foun-ders’ Committee.

Deadline is Dec 31, 2010. A papermay be nominated by an author or anymember of ISBA or SBSS. Please seehttp://bayesian.org/awards/MitchellPrize.htmlfor submission details.

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ISBA Bulletin, 17(3), September 2010 ARNOLD ZELLNER 1927-2010

ARNOLD ZELLNER 1927-2010

OBITUARY

Siddhartha Chib & John Gewekechib@wustl.edu

John.Geweke@uts.edu.au

Arnold Zellner, co-founder of ISBA and its firstpresident, passed away at his home in Chicagoon August 10, 2010 at the age of 83. One of theworld’s most prolific and influential econome-tricians, publishing more than 200 articles and22 books and monographs, he made pioneeringcontributions to Bayesian statistics, the analysisof simultaneous equation models and time seriesanalysis.

He was born on January 2, 1927 in Brooklyn,New York. He attended Harvard University on ascholarship, earning a Bachelors degree in Phy-sics in 1949. Following two years of military ser-vice, he attended graduate school at the Univer-sity of California, Berkeley, where he earned aPh.D. in Economics in 1957.

Zellner held faculty appointments in the De-partment of Economics at the University of Wa-shington (1955-1960) and the University of Wis-consin (1961-1966). In 1966 he took up the positi-

on of HGB Alexander Professor in the Universityof Chicago Business School, where he remaineduntil his retirement in 1996.

It was at the University of Washington thathe developed the seemingly unrelated regressi-ons model, a model that was the basis for nume-rous applications and subsequent developmentsin the analysis of multivariate outcomes. His con-tributions to Bayesian econometrics and stati-stics, which would be the theme of his researchfor the balance of his academic career, began witha paper published in 1964 with his University ofWisconsin colleague George Tiao.

Zellner’s landmark book, An Introduction toBayesian Inference in Econometrics, was publis-hed by John Wiley and Sons in 1971. This bookpioneered the field of Bayesian econometrics. Pu-blished at a time when few econometricians utili-zed Bayesian statistics, it demonstrated how pro-blems of concern to economists, and researchersmore broadly, could be tackled from a Bayesianperspective. The book had a large impact on thesubsequent growth of the field.

Zellner’s hallmark energy, enthusiasm and or-ganizational skills are reflected most significant-

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ISBA Bulletin, 17(3), September 2010 JULIAN BESAG 1945-2010

ly in two organizations that he founded. The on-going Seminar on Bayesian Inference in Econo-metrics and Statistics (SBIES), launched in 1971,was a key forum for many developments in Baye-sian econometrics and statistics. Following up onhis belief that Bayesian methods were useful inall scientific endeavors, Arnold helped to foundISBA in 1991. He was its first president, a privile-ge of which he remembered fondly.

Zellner was instrumental in the founding ofthe Journal of Econometrics in the early 1970’s.Due in no small part to his vigorous leadership,serving as editor until his passing, the Journal isnow a major publication in the field. Zellner al-so made outstanding contributions to the Ameri-can Statistical Association, where he inaugurated

the Journal of Business and Economic Statisticsin 1983. He was Chair of the Business and Eco-nomics Section in 1982 and went on to be elec-ted the Association President in 1991. The annualZellner Thesis Award in Business and EconomicStatistics recognizes both these contributions andhis influence on scholarship in econometrics. Hewas an elected fellow of the International Stati-stical Institute as well as the American StatisticalAssociation.

Arnold leaves a legacy of warmth, creativityand enthusiasm for the Bayesian profession andlife in general that will long live in the mindsof those who knew him. He will be greatly mis-sed.s

JULIAN BESAG 1945-2010

Photo courtesy of Larissa Stanberry.

OBITUARY

Peter GreenP.J.Green@bristol.ac.uk

Julian Besag died on 6 August 2010, in hospi-tal in Bristol, UK, following surgery after some

years of ill-health. His contributions to the dis-cipline of statistics are profound. While he didnot describe himself as a card-carrying Bayesi-an, his originality and creativity in areas thatbecame central to modern Bayesian modellingand computation have been, and continue to be,of far-reaching consequence: Julian was using

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ISBA Bulletin, 17(3), September 2010 JULIAN BESAG 1945-2010

”full conditionals” in modelling and computati-on from the mid-70s!

Julian’s research work had authority and greatoriginality. He seldom wrote the last word on asubject, but was there at the start of many of thekey developments in modern stochastic model-ling. He did not write a great deal, but his workwas very deliberate, and densely and painsta-kingly written; he had very high standards overwhat he put his name to.

The general area where he made the biggestimpact is the conditional modelling of spatial sy-stems. Together with the parallel, independent,work on interaction in contingency tables by Dar-roch, Lauritzen and Speed, Julian Besag’s work inthe 70s and early 80s laid the foundations for theentire contemporary tradition of highly structu-red stochastic systems. This strategy for buildingcomplex global models through local specificati-ons guaranteed to be self-consistent has made ahuge impact on stochastic modeling in many are-as of science, medicine and technology, and sti-mulated important work on statistical inferencefor such systems, and on their probabilistic theo-ry.

What began in his early work as a quest for fle-xible and mathematically-sound models for eco-logical phenomena, resulting in the notion of mo-delling spatial systems as Markov random fieldsled on directly to the idea of MRFs as a ge-neric model for interacting systems, with Julianhimself responsible for adapting the models andthe methodology to agricultural field trials, ar-chaeological problems, image analysis and disea-se mapping, and to hierarchical versions of suchmodels under the Bayesian paradigm.

The second major plank to his research was in-novatory work on inferential methods for spatialsystems, and their computational implementati-on. Especially noteworthy are the notion of pseu-dolikelihood in interacting systems, as a com-putationally tractable alternative to the true li-kelihood, the ’iterated conditional modes’ algo-rithm, important contributions to the algebra of

interacting systems and his role as one of the veryearly proponents of Markov chain Monte Carlomethods for fitting statistical models. In particu-lar, he was well ahead of his time in recognisingthe duality between the conditional specificationof stochastic models and the construction of al-gorithms for such models using these conditionalspecifications.

Julian was an inspiring and challenging men-tor to younger researchers; his career shows astring of papers written jointly with more juni-or people, most of whom have since developedinto independent researchers with substantial re-putations of their own.

Julian was born in Loughborough, UK, on 26March 1945, and after his mother died broughtup by German-speaking grandparents. He beganstudying engineering at Cambridge but movedto the University of Birmingham to study stati-stics, obtaining his BSc in 1968. He then spent ayear as a research assistant to Maurice Bartlettin Oxford before obtaining a lectureship at Liver-pool. He moved to Durham in 1975, where he be-came a professor in 1986. He was a visiting pro-fessor at the University of Washington in Seattleduring 1989-90 and, after a year back in Newcast-le, returned to Seattle long-term. He officially re-tired from Seattle in 2007 but remained an emeri-tus professor. He returned to the UK, and during2007-09 he held a chair at the University of Bath.At his death in 2010 he was a visiting professor atboth Bath and Bristol.

Outside his working life, Julian’s passions we-re for mountaineering (including a 14000’ peakin the Alps scaled on his honeymoon with Vale-rie), hockey (while he was at Liverpool he had atrial for the Welsh national team), running, and,after moving to Seattle, sailing. He lived on thewater in Seattle and owned two ocean-going sail-boats. His personal relationships - emotional, so-cial and professional - were often stormy and ve-ry demanding for all concerned, but he was anunforgettable character, who will be much mis-sed.s

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ISBA Bulletin, 17(3), September 2010 ANNOTATED BIBLIOGRAPHY

ANNOTATED BIBLIOGRAPHY

COMPUTATIONAL INFERENCEFOR INVERSE PROBLEMS

Tiangang Cuitcui001@aucklanduni.ac.nz

Colin Foxfox@physics.otago.ac.nz

Inverse problems occur when indirect obser-vations d are made of a physical system x, typi-cally by scattering energy of some kind, and wewant to infer properties of the unknown true sy-stem. That includes nearly all physical imagingsystems, with well-known examples being x-rayimaging, CAT scanning, and remote sensing.

The forward map F that models the determi-nistic mapping x 7! d is the subject of theoreticalphysics, though often requires further physicalmodelling of instrumentation and data model-ling as practiced in statistics. Computer imple-mentation of these models is usually the realm ofspecialists in computational methods, with effi-cient implementation being essential when com-prehensive Bayesian inference is planned. A sui-table probabilistic model for this modelling andobservation process is then

d = F (x) + b(x) + ✏.

Here, b and ✏ denote the model bias and themeasurement noise, respectively. The model bi-as may arise from various sources: numerical er-ror in computer implementation of the forwardmodel, spatial discretization of the unknown pa-rameters, inappropriate assumptions in the ma-thematical model, etc.

The system can often be represented in termsof spatially distributed parameters x (for exam-ple, coefficients in a partial differential equationmodelling energy propagation), so representati-ons and prior models are usefully drawn fromspatial statistics.

• Hurn, M. A., Husby, O., & Rue, H. (2003)“Advances in Bayesian Image Analysis”,Highly Structured Stochastic Systems, edsGreen, P. J., Hjort, N., & Richardson. S, Ox-ford University Press, 302-322.

A great overview of modern Bayesianimage modelling and analysis. The notions

of low-, mid-, and high-level models are setout, as well as an example in ultrasoundimaging. As the authors note, the issue ofphysical modelling for the likelihood is notconsidered.

Many statistical papers on inverse problemshighlight the high computational cost requiredin evaluating the forward map, and hence like-lihood. However, understanding the structure ofthe forward map is critical for efficient compu-tation. Specifically, the forward map necessarilyhas an unbounded range of sensitivities, henceposterior distributions are supported on a re-latively low-dimensional manifold with correla-tion coefficients that equal 1 to within machi-ne precision. These properties give the extremesensitivity that inverse problems display to bothmeasurement and modelling errors, and makebelievable inference from measured data a chal-lenging task.

• Fox, C., Tan, S., & Nicholls, G. K. (1997) In-verse Problems, lecture notes for ELEC 404,available at http://elec.otago.ac.nz/w/images/1/19/ELEC404 Inverse Problems.pdf

This graduate course has been taken up byengineering groups around the world, asan accessible introduction to inverse pro-blems. These notes cover the basic structureand difficulties arising in inverse problems,regularization methods, simple probabilitymodels, and sample-based inference.

In common with most of Bayesian statistics,computational inference for inverse problems isa rapidly developing research field, with remar-kable work appearing each year. We have expli-citly excluded many exciting advances that wecan forecast, and instead focus on the best tech-nology available today for performing inferencein practical inverse problems. The basic structureshould be familiar to all; posterior inference im-plemented by random walk Metropolis-Hastings(MH). However, sophisticated advances in trea-ting model error and utilizing model reductionhave vastly improved the efficiency and qualityof inference.

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Model biasA major advance in recent years has been thedevelopment of frameworks for treating bias incomplex computer models. These Gaussian pro-cess (GP) based approaches allow estimation ofmodel bias, and also the possibility of fast infe-rence via GP surrogates to the forward map. The-re are difficulties in high dimensional settings,since then building GP models is expensive. Inthe context of inverse problems, these methodshave proved a practical route to improving onbiased and overly confident estimation that re-sults from artificially simplified forward maps.

• Kennedy, M. C., & O’Hagan, A. (2001)“Bayesian calibration of computer models”(with discussion), Journal of the Royal Stati-stical Society: Series B, 63, 425–464.

In this milestone paper, the model parame-ters x are considered as a combination ofunknown calibration parameters and con-trollable inputs. By collecting field obser-vations at various controllable inputs, andpre-evaluating the forward model F at acollection of model parameters, Gaussianprocess (GP) surrogates to F are construc-ted, with the bias b represented by a se-parate GP model. Thus, the posterior dis-tribution is a joint Gaussian distributionconditioned on field observations and pre-evaluated model outputs. Estimating mo-del parameters and hyperparameters of theGP models facilitates predictions and ana-lysis of uncertainty in the forward model.

• Bayarri, M. J., Berger, J. O., Cafeo, J., Garcia-Donato, G., Liu, F., Palomo, J., Parthasa-rathy, R., Paulo, R., Sacks, J., & Walsh, D.(2007) “Computer Model Validation withFunctional Output”, Annals of Statistics,35(5), 1874–1906.

The framework of Kennedy and O’Hagan(2001) is extended to analyze vehicle sus-pension systems where the model outputis a time series. Wavelet decomposition isused to decompose the model output, withmodel output, bias function and field ob-servations transformed into the decompo-sed basis. Hence, the framework of Kenne-dy and O’Hagan (2001) can be expressedindependently on each basis.

• Higdon, D., Gattiker, J., Williams, B., &Rightley, M. (2008) “Computer Model Ca-

libration using High Dimensional Output”,Journal of the American Statistical Association,103(482), 570–583.

Implosion of steel cylinders are analyzed.To deal with the functional model output,singular value decomposition is used toconstruct the basis vector for the model out-puts. Then, a different set of basis vector arechosen for the bias function, based on whatis known about actual physical process andpotential deficiencies in the simulator.

• Higdon, D., Lee, H., & Holloman, C. (2003)“Markov chain Monte Carlo-based approa-ches for inference in computationally inten-sive inverse problems”, Bayesian Statistics 7,eds Bernardo, J. M., Bayarri, M. J., Berger, J.O., Dawid, A. P., Heckerman, D., Smith, A.F. M., & West, M., Oxford University Press,181–197.

A Metropolis couple scheme is implemen-ted to analyze the permeabilities of a sub-surface flow system. Since measurementscan only be observed for a single set of con-trollable inputs, the framework of Kenne-dy and O’Hagan (2001) is not applicable.Hence the nature and size of the model biascan only be judged by knowledge about thephysical process.

We note that in practice the forward modelis often ‘corrected’ after examination of residualstructure, also using modelling judgements.

Proposals and adaptation• Nicholls, G. K., & Fox, C. (1998) “Prior mo-

delling and posterior sampling in impe-dance imaging”, Bayesian Inference for Inver-se Problems, Proc. SPIE 3459, 116–127.

Several proposal distributions built frommultiple ‘moves’ are applied to samplingin electrical impedance tomography, un-der various prior models. Demonstrates thecomputational gain available by exploitingthe structure of the inverse problem whendesigning proposal distributions.

• Green, P. J., & Mira, A. (2001) “Delayedrejection in reversible jump Metropolis-Hastings”, Biometrika, 88, 1035–1053.

The reversible jump formalism is presented

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in a form that we use to evaluate the Ha-stings’ ratio for subspace moves. The de-layed rejection algorithm is also discussed.

Tuning of proposals is often required to gi-ve statistically efficient implementations, thoughcan be very time consuming for the high dimen-sional representations that are typical in inverseproblems. The advent of adaptive methods gavea practical way to automate that tuning process.

• Haario, H., Saksman, E., & Tamminen,J. (2001) “An adaptive Metropolis algo-rithm”, Bernoulli, 7, 223–242.

This adaptive Metropolis algorithm usesthe multivariate Gaussian N(0, 2.832

d ⌃) asproposal, where d is dimensionality of theparameter space, with the covariance ma-trix ⌃ estimated from past samples of thechain. Ergodicity is established by an argu-ment akin to lace work.

• Haario, H., Laine, M., Mira, A., & Saks-man, E. (2006) “DRAM: Efficient adaptiveMCMC”, Statistics and Computing, 16, 339–354.

Combines the delayed rejection and adap-tive Metropolis algorithms.

• Roberts, G. O., & Rosenthal, J. S. (2007)“Coupling and Ergodicity of AdaptiveMCMC”, Journal of Applied Probability, 44,458–475.

Establishes simplified regularity conditionsthat are sufficient to ensure ergodicity ofadaptive MCMC algorithms, namely, thediminishing adaptation and bounded con-vergence conditions. Now, anybody can de-sign a convergent adaptive MCMC.

• Roberts, G. O., & Rosenthal, J. S. (2009)“Examples of Adaptive MCMC”, Journal ofComputational and Graphical Statistics, 18(2),349–367.

Based on the above ergodicity conditions,several adaptive MCMC algorithms are de-signed including the adaptive Metropoliswith Gibbs algorithm and the Regional ad-aptive Metropolis algorithm that tunes ran-dom walk proposals to match an ‘optimal’acceptance rate. These two algorithms po-tentially can be applied in a high dimensio-nal context.

• Higdon, D., Reese, C. S., Moulton, J. D.,Vrugt, J. A., & Fox, C. (2009) “Posterior ex-ploration for computationally intensive for-ward models”, Technical Report LA-UR 08-05905, Statistical Sciences Group, Los AlamosNational Laboratory, to appear in The Hand-book of Markov Chain Monte Carlo, eds Meng,X.-L., Gelman, A., & Jones, G., CRC press.

Several implementations of the MH are re-viewed and tested on a synthetic 2D elec-trical impedance tomography problem. In-terestingly, the multivariate Gaussian pro-posal used by the adaptive Metropolis al-gorithm gives poor mixing, while simplesingle-site update shows acceptable mi-xing.

The following two algorithms provide usefulblack-box sampling tools for low dimensionalmid-level representations of fixed dimension.

• Ter Braak, C. J. F. (2006) “A Markov ChainMonte Carlo version of the genetic algo-rithm differential evolution: easy Bayesiancomputing for real parameter spaces”, Sta-tistics and Computing, 16, 239–249.

A population MCMC algorithm is desi-gned by combining the genetic algorithmdifferential evolution and the random walkMH algorithm.

• Christen, J. A., & Fox, C. (2010) “A gene-ral purpose sampling algorithm for conti-nuous distributions (the t-walk)”, BayesianAnalysis, 5(2)263–282.

Two points in state space are maintainedand used to design moves for the t-walkthat is close to affine invariant.

Efficient MCMCOne major difficulty in MCMC sampling for in-verse problems is the computing cost of each ite-ration in the MH. To make a fast inference, areduced order model can be employed to mi-mic the behavior of the posterior distribution. Aswe already mentioned, GP models are possiblecandidates but limited by the dimensionality ofthe parameter space. Alternatively, we can usethe knowledge of the physical modelling of theforward map to build a reduced order model,which could provide reasonable numerical accu-racy and feasible computing speed.

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• Christen, J. A., & Fox, C. (2005) “MCMCusing an approximation” Journal of Compu-tational and Graphical Statistics, 14(4), 795–810.

This paper presents our preferred methodfor taking advantage of computational effi-ciencies available from state-dependent ap-proximations, while still sampling from thecorrect posterior distribution. An exampleis presented of inference in an inverse pro-blem using a cheap approximation basedon a local linearization of the forward map.

• Kolehmainen, V., Tarvainen, T., Arridge, S.R., & Kaipio, J. P. (2010) “Marginalizationof uninteresting distributed parameters ininverse problems”, International Journal forUncertainty Quantification, 1, accepted.

Develops the enhanced error model, eva-luated over the prior distribution. Presentsan application in which the forward mapis approximated by a coarse numerical mo-del, with statistics of the numerical errorbetween the coarse model and the forwardmap used to construct an accurate reducedorder model of the posterior distribution.

• Cui, T. (2010) “Bayesian calibration of geo-thermal reservoir modelling via Markovchain Monte Carlo”, Ph.D. thesis, Depart-ment of Engineering Science, the Universi-ty of Auckland.

Presents detailed development of the ad-aptive delayed-acceptance Metropolis Ha-stings (ADAMH) algorithm, with exten-sive performance verification and large-scale examples. This algorithm representsour best current technology for correct andcomplete sampling in inverse problemsusing low-level representations. A milesto-ne for geothermal model calibration, allo-wing automatic calibration of large-scalemodels for the first time.

Convergence diagnostics• Roberts, G. O. (1996) “Markov chain con-

cepts related to sampling algorithms”, inMarkov Chain Monte Carlo in Practice, edsGilks, W. R. and Richardson, S., & Spiegel-halter, D. J., Chapman & Hall, 45–57.

A useful summary of convergence resultsfor MCMC, with simple window estima-tors for the variance in the sample mean.

• Wolff, U. (2004) “Monte Carlo errors withless errors”, Computer Physics Communicati-ons, 156(2), 143–153.

Introduced a �-method to estimate the va-riance of the mean estimator, and the inte-grated autocorrelation time, by choosing anoptimal window size that minimizes the er-ror of the Monte Carlo error for a given se-quence of correlated samples. MATLAB co-de is available on the author’s website.

Selected applicationsWe list here, without annotation, papers that de-monstrate exemplary modelling and inferentialsolution of substantial inverse problems.

• Haario, H., Laine, M., Lehtinen, M., Saks-man, E., & Tamminen, J. (2004) “Markovchain Monte Carlo methods for high di-mensional inversion in remote sensing”,Journal of the Royal Statistical Society: SeriesB, 66(3), 591–608.

• Cornford, D., Csato, L., Evans, D. J., & Op-per, M. (2004) “Bayesian analysis of thescatterometer wind retrieval inverse pro-blem: Some new approaches”. Journal of theRoyal Statistical Society. Series B, 66(3), 609–652.

• McKeague, I. W., Nicholls, G. K., Speer, K.,& Herbei, R. (2005) “Statistical inversionof South Atlantic circulation in an abyssalneutral density layer”, Journal of Marine Re-search, 63(4), 683–704.

• Watzenig, D., & Fox, C. (2009) “A reviewof statistical modeling and inference forelectrical capacitance tomography”, Mea-surement Science and Technology, 20(5), doi:10.1088/0957-0233/20/5/052002.

• Lipponen, A., Seppanen, A., & Kaipio, J. P.(2010) “Reduced-order estimation of non-stationary flows with electrical impedancetomography”, Inverse Problems, 26(7), doi:10.1088/0266-5611/26/7/074010.s

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ISBA Bulletin, 17(3), September 2010 SOFTWARE HIGHLIGHT

SOFTWARE HIGHLIGHT

RECENT DEVELOPMENTS IN THEBUGS SOFTWARE

Dave Lunn,MRC Biostatistics Unit

Cambridge, UK

david.lunn@mrc-bsu.cam.ac.uk

In recent years the main thrust of the BUGS pro-ject [1, 2] has been towards focussing develop-ment efforts on the open-source incarnation ofthe software, OpenBUGS. The main differencebetween OpenBUGS and WinBUGS lies in thebehaviour of the ’expert system’, which choosesthe various sampling methods for updating un-known variables in the model. WinBUGS definesone algorithm for each possible classification ofsampling distribution whereas there is no limitto the number of algorithms that OpenBUGS canmake use of. The OpenBUGS approach providesmuch greater scope for experimenting with con-temporary MCMC methods and has thus beenadopted as the way forward for the project. Atthe time of writing, OpenBUGS already supportsconsiderably more sampling methods than doesWinBUGS, such as several new types of block-updater and some ’population MCMC’ methods[3].

Another reason for the shift towards OpenBUGSis the open-source nature of the software, poten-tially facilitating developments from many indi-viduals. As the software has matured, from so-mewhat experimental into a stable and reliablepackage, much behind-the-scenes work has ledto an appropriate infrastructure for supportingsuch developments. The source code now resideson SourceForge [4], an internet repository spe-cifically designed for such projects. Anyone candownload the source code and apply for ”devel-oper” status if they have code to contribute. Inaddition a wiki page has been developed [5] tofacilitate, for example, distribution, disseminati-on, discussion and bug tracking, and also to actas a general BUGS resource, providing FAQs andinstructions on how to get the software runningon Linux, or how to begin developing the soft-ware yourself, say. As a wiki this has the faci-lity to be edited from anywhere and by anyone

(with permission), representing another progres-sive move to take the project forwards. A financehub has been set up so that the project can nowreceive donations and an international steeringcommittee will decide on future directions, suchas how to spend donated funds to best serve theuser community.

In terms of developing the capabilities of the soft-ware, here at the MRC Biostatistics Unit in Cam-bridge we have been focussing on translating so-me of the more specialised WinBUGS develop-ments into OpenBUGS, such as templates for im-plementing your own specialised functions anddistributions (WBDev [6]), and reversible jumpcapabilities (Jump [6]). Mostly we have been im-plementing and developing facilities for model-ling dynamical systems -models involving diffe-rential equations- and combining these effective-ly with other models. We are also looking at im-plementing truncated distributions correctly, sothat there is a clearer distinction between censo-ring and truncation [7]. Meanwhile, aside fromhis important work on improving the expert sy-stem, implementing new MCMC methods, andgeneral refinement of the system, Andrew Tho-mas has been working on methods for sequenti-al importance sampling and parallelising multi-ple Markov chains across multi-core processors,leading to ”OpenSIS” and ”MultiBUGS”, respec-tively. Also here at the Biostatistics Unit, ChrisJackson has made several important contribu-tions, including techniques for visualising theuncertainty conveyed by posterior distributions[8], current work-in-progress on Bayesian non-parametrics, and collaborating with Uwe Ligges(Technische Universitat Dortmund) and othersto produce a new version of BUGS-R (an inter-face for controlling OpenBUGS from within R,aka BRugs). Vijay Kumar (Gorakhpur Universi-ty) has contributed numerous specialized distri-butions, such as various extensions of the Wei-bull distribution. And last, but by no means least,a team led by Neal Thomas (Pfizer, US) and BillGillespie (Metrum Institute, Connecticut) havedeveloped a suite of validation programs for thesoftware, to prove that it is actually working cor-rectly!

OpenBUGS is now considered to be at least as re-

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ISBA Bulletin, 17(3), September 2010 STUDENTS’ CORNER

liable and efficient as WinBUGS 1.4.3 (no jokesplease) across a wide range of test applications.WinBUGS will always be available but is no lon-ger being developed, and so we are urging usersto make the switch. Please take a look anyway, byvisiting the wiki page [5]; comments are alwaysvery welcome.

Acknowledgements

Thank you to everyone involved in the project,there are too many to mention here! The followi-ng people have been instrumental in getting theproject to where it is today: Andrew Thomas, Da-vid Spiegelhalter, Nicky Best, Bob O’Hara, NealThomas, Bill Gillespie, Nathan Coulter, Uwe Lig-ges, Larry Gould, Chris Jackson and Steve Miller.Many thanks also to all the users of the software,whose patience, enthusiasm and innovation is al-ways very much appreciated.

References

[1] D. Lunn, D. Spiegelhalter, A. Thomas, and N.Best. The BUGS project: Evolution, critique and

future directions (with discussion). Statistics inMedicine 28:3049–3067, 2009.

[2] D. J. Lunn, A. Thomas, N. Best, and D. Spie-gelhalter. WinBUGS -a Bayesian modelling fra-mework: concepts, structure, and extensibility.Statistics and Computing 10:325–337, 2000.

[3] C. J. F. Ter Braak. A Markov chain Monte Carloversion of the genetic algorithm Differential Evo-lution: Easy Bayesian computing for real parame-ter spaces. Statistics and Computing 16:239–249,2006.

[4] http://sourceforge.net

[5] http://www.openbugs.info

[6] http://www.winbugs-development.org.uk

[7] https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind0910&L=BUGS&F=&S=&P=5643

[8] C. H. Jackson. Displaying uncertainty withshading. The American Statistician 62:340-347,2008.s

STUDENTS’ CORNER

Call for Dissertation AbstractsLuke Bornn

l.bornn@stat.ubc.ca

Recent Ph.D graduates, having your disserta-tion abstract published is as simple as emailingit to the email address above. Publishing yourabstract will not only provide exposure for your

research, but it may potentially lead to collabo-rations with future colleagues. In addition, youare providing an important service to the Bayesi-an community by giving established researchersa taste of the interests of young researchers. Fa-culty, please encourage your students’ participa-tion.s

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ISBA Bulletin, 17(3), September 2010 NEWS FROM THE WORLD

NEWS FROM THE WORLD

CALL FOR ANNOUNCEMENTS

Sebastien Haneusehaneuse.s@ghc.org

I would like to encourage those who have anyannouncements or would like to draw attentionto an up-coming conference, to get in touch withme and I would be happy to place them here.

Announcements

2012 ISBA World Meeting Planning has alrea-dy begun for the 11th ISBA World Meeting, tobe held in June 2012 in Kyoto, Japan. See theJune 2010 issue of the ISBA Bulletin for the an-nouncement and more information http://www.bayesian.org/bulletin/1006.pdf.

I would like to encourage those who have anyannouncements or would like to draw attentionto an up-coming conference, to get in touch withme and I would be happy to place them here.

Meetings and conferences

Eighth ICSA International Conference,Guangzhou, China. 19-22nd December, 2010.

The conference will be held at Guangzhou Uni-versity, located in Guangzhou city in Guang-dong Province in Southern China and has a the-me ‘Frontiers of Interdisciplinary and Methodo-logic Statistical Research’. The conference is co-sponsored by the American Statistical Associati-on, the Chinese Association of Applied Statistics,the Chinese Society of Probability and Statistics,and the Institute of Mathematical Statistics, andInternational Society for Bayesian Analysis (IS-BA), and jointly organized by the College of Ma-thematics and Information Science, GuangzhouUniversity.

Please note that the deadline for early registra-tion is the 1st Nov. Additional information can befound at http://www.icsa2.org/Intl_2010/.

66th Annual Deming Conference on AppliedStatistics, Atlantic City, NJ. 5-10th December,2010.

The purpose of the three-day conference isto provide a learning experience on recent de-velopments in statistical methodologies in twel-ve three-hour tutorials. Attendees receive boundproceedings of the presentations. The conferenceis followed by two parallel short courses on (1)Bayesian Adaptive Clinical Trials by Prof. BradCarlin, University of Minnesota and Scott Berry,Berry Consultants; and (2) SAS for Mixed Modelsby Profs. Ramon Littell, University of Florida andWalter Stroup, University of Nebraska. The con-ference makes the books on which the conferenceis based available for sale at about a 40% dis-count.

The full program and online registrationis available at http://www.demingconference.com.

Adapski III: Advances in Monte Carlo, Park Ci-ty, UT, 3-4th January, 2011.

Following an enthustiastic reponse to the ear-lier editions of the workshop in 2005 and 2008,this workshop is intended to provide an updatedsnapshot of the methodological and theoreticaladvances in Monte Carlo methods with an em-phasis on adaptive Monte Carlo methods in thebroad sense (adaptive MCMC, adaptive popula-tion Monte Carlo, and various breeds of adaptiveimportance sampling amongst others), that is al-gorithms that attempt to automatically optimisetheir performance to a given task.

The workshop will consist of 4 half-day sessi-ons on 3rd and 4th January and one or two po-ster sessions and will be held at The Canyons.There will be breaks on both afternoons in orderto allow both informal discussions and relaxation(skiing!).

Additional information can be found athttp://www.maths.bris.ac.uk/%7Emaxca/adapskIII/.

MCMCSki III: Markov Chain Monte Carlo inTheory and Practice, Snowbird, UT, 5-7th Janua-ry, 2011.

A central theme of the conference will be Mar-kov chain Monte Carlo (MCMC) and related me-

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ISBA Bulletin, 17(3), September 2010 NEWS FROM THE WORLD

thods and applications in the 21 years since thepublication of Gelfand and Smith (1990, JASA),the paper that introduced these methods to main-stream statisticians. The conference will also fea-ture three plenary speakers (Nicky Best, MikeNewton, and Jeff Rosenthal) and six invited ses-sions from internationally known experts cover-ing a broad array of current and developing sta-tistical practice. As with the first joint IMS-ISBAmeeting in Isla Verde, Puerto Rico,and the secondand third joint meeting in Bormio, Italy, nightlyposter sessions will offer substantial opportunityfor informal learning and interaction.

Additional information can be found at http://madison.byu.edu/mcmski/

Fourth Annual Bayesian Biostatistics, Houston,Texas. 26-28th January, 2011.

Current and prospective users of Bayesian bio-statistics are invited to join others with similar in-terests for a three-day conference sponsored bythe Department of Biostatistics at The Universi-ty of Texas MD Anderson Cancer Center. Resear-chers are invited to submit abstracts for conside-ration as contributed presentations at the confe-rence. All topics in Bayesian biostatistics are ac-ceptable, including clinical trial design and ana-lysis, health policy, epidemiology, health econo-mics, clinical decision making, comparative ef-fectiveness, and bioinformatics. Submit abstracttitle; text of 150 words or less; authors, affilia-tions, and contact information via e-mail to Ly-dia Davis (lbdavis@mdanderson.org). Submissi-on Deadline: October 1, 2010. Applicants will benotified of the Program Committee’s decision byNovember 1, 2010.

All participants are encouraged to displaya poster at the conference poster sessionand discussion. If interested, send an abstract(up to 300 words) to Lydia Davis (lbda-vis@mdanderson.org) by November 15, 2010.

Online registration and additional informa-tion about Bayesian Biostatistics Conference2011 is available at http://biostatistics.mdanderson.org/BBC2011.

Conference in Honour of Professor Adrian F.M. Smith on Hierarchical Models and MarkovChain Monte Carlo, Heraklion, Greece. 2-5th Ju-ne, 2011.

In Bayesian statistics, two influential papersin the latter part of the 20th Century –reprintedin Volume III of Breakthroughs in Statistics,Springer-Verlag–were co-authored by Adrian

Smith. These papers contain two central ideas inthe theory and practice of modern Bayesian sta-tistics, namely Hierarchical Models (Lindley andSmith, Journal of the Royal Statistical Society, Se-ries B, 1972) and Markov chain Monte Carlo (Gel-fand and Smith, Journal of the American Statisti-cal Society, 1990). This conference will showca-se many key advances that continue to be made,using these ideas, by many established and youn-ger researchers.

Research involving hierarchical models andMCMC continues to grow at an astonishing ra-te, spanning a broad spectrum of topics in me-dicine, engineering, scientific computation, busi-ness, psychology, bio-informatics, computatio-nal physics, graphical models, neural networks,geosciences, and public policy. This explosion ofBayesian ideas is, in part, the result of papers au-thored or co-authored by Adrian Smith.

Additional information can be found at http://afmsmith.com/index2.htm.

International Research Conference on BayesianLearning, Istanbul, Turkey. 15-17th June, 2011.

In all domains of research, a major part of theproblem that needs to be solved involves the taskof managing the uncertainty inherent in the pro-blem. In that instance, Bayesian Learning pro-vides a powerful methodology to researchers,enabling them to reach effective decisions in lightof evidence. With its ability to incorporate pri-or knowledge to the inference process, Bayesi-an Learning appeals to researchers for both of itstheory and applications.

Interested individuals from academic andbusiness worlds are invited to come together todiscuss and communicate on challenging issuesrelated to theory of Bayesian learning and app-lications in finance & accounting, general mana-gement, marketing, organizational behavior andproduction & operations within the historic andmystic environment of Istanbul while we will ce-lebrate the 310th birthyear of Rev. Bayes.

Additional information can be found at http://marc.yeditepe.edu.tr/yircobl11.htm.

Short courses and workshops

Workshop on Bayesian Inference for LatentGaussian Models with Applications, Zurich,Switzerland. 2-5th February, 2011.

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ISBA Bulletin, 17(3), September 2010 NEWS FROM THE WORLD

Latent Gaussian models have numerous ap-plications, for example in spatial and spatio-temporal epidemiology and climate modelling.This workshop brings together researchers whodevelop and apply Bayesian inference in thisbroad model class. One methodological focusis on model computation, using either classi-cal MCMC techniques or more recent determi-nistic approaches such as integrated nested La-place approximations (INLA). A second themeof the workshop is model uncertainty, rangingfrom model criticism to model selection and mo-del averaging.

Hravard Rue will give an INLA tutorial on thefirst day. Further confirmed invited speakers areRenato Assuncao, Gonzalo Garcıa-Donato, AlanGelfand, Finn Lindgren, Douglas Nychka, Chri-stopher Paciorek and Stephen Sain. Contributedtalks and a poster session complete the four-dayprogram.

Additional information can be found at http://www.math.uzh.ch/bilgm11.

2011 International Workshop on ObjectiveBayesian Analysis, Shanghai, China. 11-15th Ju-ne, 2011.

Following earlier meetings on objectiveBayes methodology the principal objectives ofOBayes2011 are to facilitate the exchange of re-cent research developments in objective Bayesmethodology, to provide opportunities for newresearchers to shine, and to establish new col-

laborations and partnerships that will channelefforts into pending problems and open new di-rections for further study. O-Bayes2011 will alsoserve to further crystallize objective Bayes me-thodology as an established area for statisticalresearch.

Additional information can be found at http://www.sfs.ecnu.edu.cn/Obayes2011/index.html.

Eight Workshop on Bayesian Nonparametrics,Veracruz, Mexico. 26-30th June, 2011.

The workshop aims at presenting the latestdevelopments on Bayesian nonparameteric stati-stics, covering a wide range of theoretical, metho-dologic and applied areas. The meeting will bestructured in 4 tutorials on special topics, a seriesof invited and contributed talks and contributedposters sessions. For those interested this eventwill be preceded by the Mexican Workshop onBayesian Statistics (TAMEB) which will feature aday of introductory courses (in Spanish) to Baye-sian statistics.

Scientific committee: David B. Dunson, Subha-shis Ghosal, Jim Griffin, Nils L. Hjort, Michael I.Jordan, Yongdai Kim, Antonio Lijoi, Ramses H.Mena, Peter Muller, Luis E. Nieto, Igor Pruenster,Fernando A. Quintana, Yee W. Teh and StephenG. Walker

Additional information can be found at http://www.bnpworkshop.org/.s

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ISBA Bulletin, 17(3), September 2010

Executive Committee

President: Peter MullerPast President: Mike WestPresident Elect: Michael I. JordanTreasurer: Gabriel HuertaExecutive Secretary: Merlise Clyde

Program Council

Chair: Alex SchmidtVice Chair: Igor PruensterPast Chair: Herbie Lee

Board Members:

2010–2012: Siddhartha Chib, ArnaudDoucet, Peter Hoff, Raquel Prado2009–2011: David Dunson, David van Dyk,Katja Ickstadt, Brunero Liseo2008–2010: Sylvia Fruhwirth-Schnatter, Lur-des Inoue, Hedibert Lopes, Sonia Petrone

EDITORIAL BOARD

Editor

Manuel Mendozamendoza@itam.mx

Associate Editors

InterviewsDonatello Telescatelesd@u.washington.edu

Annotated BibliographyBeatrix Joneswww.massey.ac.nz/⇠mbjones/m.b.jones@massey.ac.nz

Software HighlightAlex Lewinwww.bgx.org.uk/alex/

a.m.lewin@imperial.ac.uk

Students’ CornerLuke Bornnwww.stat.ubc.ca/⇠l.bornn/l.bornn@stat.ubc.ca

News from the WorldSebastien Haneusehttp://www.centerforhealthstudies.org/ctrstaff/haneuse.html

haneuse.s@ghc.org

Content 21 www.bayesian.org

Bayesian Analysis for Population EcologyRuth King, Byron J.T. Morgan, Olivier Gimenez,

and Stephen P. BrooksCatalog no. K10597, January 2010, 456 pp.

ISBN: 978-1-4398-1187-0, $79.95 / £49.99

Applied Bayesian Hierarchical MethodsPeter D. Congdon

Catalog no. C7206, May 2010, 604 pp.ISBN: 978-1-58488-720-1, $89.95 / £57.99

Time SeriesModeling, Computation, and Inference

Raquel Prado and Mike WestCatalog no. C9336, May 2010, 368 pp.

ISBN: 978-1-4200-9336-0, $89.95 / £57.99

Bayesian Model Selection and Statistical Modeling

Tomohiro AndoCatalog no. K11633, May 2010, 300 pp.

ISBN: 978-1-4398-3614-9, $89.95 / £63.99

Statistical InferenceAn Integrated Bayesian/Likelihood Approach

Murray AitkinCatalog no. C3436, June 2010, 254 pp.

ISBN: 978-1-4200-9343-8, $89.95 / £57.99

Bayesian Ideas and Data AnalysisAn Introduction for Scientists and StatisticiansRonald Christensen, Wesley Johnson, Adam Branscum,

and Timothy E. HansonCatalog no. K10199, July 2010, 516 pp.

ISBN: 978-1-4398-0354-7, $69.95 / £44.99

Bayesian Adaptive Methods for Clinical Trials

Scott M. Berry, Bradley P. Carlin, J. Jack Lee, and Peter MüllerCatalog no. K11217, July 2010, 323 pp.

ISBN: 978-1-4398-2548-8, $89.95 / £57.99

Bayesian Modeling in BioinformaticsEdited by

Dipak K. Dey, Samiran Ghosh, and Bani K. MallickCatalog no. C7017, September 2010, c. 466 pp.

ISBN: 978-1-4200-7017-0, $89.95 / £57.99

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