DEMORALIZATION AND HARM PREVENTION PLANNING Copyright ©2011, Indiana University Conscience Project.
Methods and Uses of Graph Demoralization
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Methods and Uses of Graph Demoralization
Mary McGlohonSIGBOVIK
April 1, 2007
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Motivation
• Moralization is an important tool in probabilistic graphical models
• The method of demoralization has not been properly addressed in research. Oh noes!
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Demotivation
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Outline for talk
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Preliminaries: PGMs
• Probabilistic models can be represented by graphs.• Nodes = Random Variables• Edges = Dependencies between RVs
Rain Temperature
PlayTennis
EnjoySport
Usual graph terms apply (parents, children, ancestors, descendents, cycles...)
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Preliminaries: PGMs
• Each node has its very own conditional probability table.
Rain Temperature
PlayTennis
EnjoySport
T F
.8 .2
Hi Lo
.6 .4
Rain Temp PT=T PT=F
T Hi .2 .8
T Lo .3 .7
F Hi .4 .6
F Lo .1 .9PT ES=T ES=F
T .8 .2
F 0 1
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Graph Moralization
• To convert from directed to undirected graphical model, it is necessary to moralize the graph.
X1 X2
X4X3
X5 X6
X7
Unmarried parents = immorality
We’re living in sin!
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Graph Moralization
• To convert from directed to undirected graphical model, it is necessary to moralize the graph.
X1 X2
X4X3
X5 X6
X7
Unmarried parents = immorality
We’re living in sin!
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Graph Moralization
• To convert from directed to undirected graphical model, it is necessary to moralize the graph.
X1 X2
X4X3
X5 X6
X7
Marry the parents = moralize
Saved by the power of Jesus!
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Graph Moralization
• To convert from directed to undirected graphical model, it is necessary to moralize the graph.
X1 X2
X4X3
X5 X6
X7
Marry the parents = moralizeThen un-direct edges.
Disclaimer: The moral judgments represented by this preliminary section do not necessarily represent those of the author or the NSF.
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Graph Demoralization
• 3 methods for demoralizing
X1 X2
X4X3
X5 X6
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Isolation
• Based on social group theory
X1 X2
X4X3
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Isolation
X1 X2
X4X3
X5 X6
X7
Choose node(s) to isolate,Remove all edges to/from nodes.
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Isolation
X1 X2
X4X3
X5 X6
X7
1 graph 5 separate graphs!Probability distribution is totally screwed!
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Misdirection
• Also based on social group theory
X1 X2
X4X3
X5 X6
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Misdirection
X1 X2
X4X3
X5 X6
X7
Remove edge, direct it off the page.
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Misdirection
X1 X2
X4X3
X5 X6
X7
Remove edge, direct it off the page.
Confuses probability distribution! Very demoralizing!
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Disbelief Propagation
X1 X2
X4X3
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Condition disbelief on a node,Propagate disbelief through graph.
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Disbelief Propagation
X1 X2
X4X3
X5 X6
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Awww.....
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Applications
• Sating sadistic susceptibility of statisticians
More important than you’d think!
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Statisticians are mean!
• The word “statistics” is nearly impossible to pronounce while drunk.
• But, stat homework is only tolerable in such an inebriated state.
E(statisticians)
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Statisticians are mean!• Turf war between frequentists and
Bayesians• Rap battle between The Unbiased M.L.E. and Emcee MC
This is a Bayesian House.
I can say with 95% confidence
that your ass will contain my
foot.
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Conclusions
• Three methods for graph demoralization– Isolation– Misdirection– Disbelief Propagation
• Useful because statisticians like demoralizing things.
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References
[1] A. Arnold. Chronicles of the Bayesian-Frequentist Wars. somewhere in Europe with .75 probability, 1999.
[2] C. Bishop. Pattern Recognition and Machine Learning: 23 cents cheaper per page than Tom Mitchell's book. Springer Texts, New York, 2006.
[3] K. El-Arini. Metron’s Bayesian Houses. In Machine learning office conversations, 2007.
[4] D. Koller and N. Friedman. Probabilistic Graphical Models (DRAFT). Palo Alto, CA, 2007.
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References
[4] T. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
[5] E. Stiehl. Misdirected and isolating groups and their subsequent demoralization. Conversations with resident business grad student at Machine Learning Department holiday parties, 2006.
[6] L.Wasserman. All of Statistics. Pink Book Publishing, New York.
[7] L. Wasserman and J. Lafferty. All of Statistical Ma-chine Learning. (DRAFT) Pink Book Publishing, New York.
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Questions?