Variational Bayes 101

Informatics and Mathematical Modelling / Lars Kai Hansen

Adv. Signal Proc. 2006

Variational Bayes 101



The Bayes scene Exact averaging in

discrete/small models (Bayes networks)

Approximate averaging: - Monte Carlo methods - Ensemble/mean field

- Variational Bayes methods

Variational-Bayes .orgMLpediaWikipedia

• ISP Bayes:

ICA: mean field, Kalman, dynamical systemsNeuroImaging: Optimal signal detectorApproximate inferenceMachine learning methods



Bayes’ methodology

Minimal error rate obtained when detector is based on posterior

probability (Bayes decision theory)

( | ) ( )( | ) , | 1,..,

( ) n

P D M P MP M D D x n N

P D

Likelihood may contain unknown parameters

( | ) ( | ) ( | )

[ ( | )] ( | )nn

P D M P D p M d

P x p M d



Bayes’ methodology

Conventional approach is to use most probable parameters

* *( | ) ( | ) ( | ) ( | )n nn n

P D M P x M P x p M However: averaged model is generalization optimal (Hansen, 1999),

i.e.:

( | ) ,( | ) arg max log ( | )BayesianAverage P x D d D

P x D P x M



The hidden agenda of learning

Typically learning proceeds by generalization from limited set of samples…but

We would like to identify the model that generated the data

….Choose the least complex model compatible with data

That I figured

out in 1386



Generalizability is defined as the expected performance on a random new sample ... the mean performance of a model on a ”fresh” data set is an unbiased estimate of generalization

Typical loss functions: <-log p(x)> , < # prediction errors > < [ g(x)-ĝ(x) ] 2 >, <log p(x,g)/p(x)p(g)>, etc

Results can be presented as ”bias-variance trade-off curves” or ”learning curves”

Generalization!



Generalization optimal predictive distribution

”The game of guessing a pdf” Assume: Random teacher drawn from P(θ), random

data set, D, drawn from P(x|θ) The prediction / generalization error is

( , , ) [ log ( | , )] ( | )

( ) ( , , ) ( ) ( | )

D A p x D A P x dx

A D A P P D d dD

Predictive distribution of model A Test sample distribution



Generalization optimal predictive distribution

We define the ”generalization functional” (Hansen, NIPS 1999)

Minimized by the ”Bayesian averaging” predictive distribution

[ (. | .,.)] log ( | ) ( | ) ( | ) ( )

( )[ ( | ) 1]

H q q x D P x dxP D dDP d

D q x D dx dD

( | ) ( )( | ) ( | )

( | ') ( ') '

P D Pq x D P x d

P D P d



Bias-variance trade-off and averaging

Now averaging is good, can we average ”too much”?

Define the family of tempered posterior distributions

Case: univariate normal dist. w. unknown mean parameter…

High temperature: widened posterior average

Low temperature: Narrow average

1/

1/

( ( | ) ( ))( | , ) ( | )

( ( | ') ( ')) '

T

T

P D Pq x D T P x d

P D P d



Bayes’ model selection, example Let three models A,B,C be given

A) x is normal N(0,1) B) x is normal N(0,σ2), σ2 is uniform U(0,∞) C) x is normal N(μ,σ2), μ, σ2 are uniform U(0,∞)

2 2 22

1

1 N

n x xn

m xN

1

1 N

x nn

xN

2 2

1

1( )

N

x n Xn

xN



Model A

The likelihood of N samples is given by

/ 2

21( | ) exp

2 2

NNm

P D A



Model B


2 2 2

0

/ 2

222 2

2

222

( | ) ( | 0, ) ( )

1exp

2 2

22

2 2

N

NN

P D A P D P d

Nmd

NmN



Model C


2 2 2

0

/ 2 2 22

2 2

31 21 22 2

( | ) ( | , ) ( , )

[( ) ]1exp

2 2

32

2 2

N

X X

NN

X

P D A P D P d d

Nd d

NNN



•Bayesian model selection•C(green) is the correct model,

what if only A(red)+B(blue) are known?



•Bayesian model selection•A (red) is the correct model



Bayesian inference• Bayesian averaging

• Caveats: Bayes can rarely be implemented exactly

Not optimal if the model family is incorrect: ”Bayes can not detect bias”

However, still asymptotically optimal if observation model is correct & prior is ”weak” (Hansen, 1999).

( | , ) ( | , ) ( | , ) ,

ˆ( | , ) ( | , ( ))

p g x D p g x p x D d

p g x D p g x D



Hierarchical Bayes models• Multi-level models in Bayesian averaging

( | , ) ( | , ) ( | , , ) ( | , ) ,

ˆ ˆ( | , ) ( | , ( , ( )))

p g x D p g x p x D p x D d d

p g x D p g x D D

C.P. Robert: The Bayesian Choice - A Decision-Theoretic Motivation.Springer Texts in Statistics, Springer Verlag, NewYork (1994).

G. Golub, M. Heath and G. Wahba, Generalized crossvalidationas a method for choosing a good ridge parameter,Technometrics 21 pp. 215–223, (1979).

K. Friston: A theory of Cortical Responses. Phil. Trans. R. Soc. B 360:815-836 (2005)



Hierarchical Bayes models

posterior( ) prior( )

( | , ) ( | , ) ( | , ) ( | ) ,

( | ) ( | )( | , )

( )

( | ) ( ) exp( ( ))

( | ) ( | ) ( | )

( ) ( )

p g x D p g x p D p D d d

p D pp D

p D

p C f

p D p D p d

f f

“learning hyper-

parameters by adjusting prior expectations”

-empirical Bayes-MacKay, (1992)

Hansen et al. (Eusipco, 2006)Cf. Boltzmann learning (Hinton et al. 1983)

Posterior

“Evidence”

Prior

Target atMaximal evidence



Hyperparameter dynamics

2

2 2

posterior( ) prior( )

2 2,

2

2,

( | ) ( ) exp( )

1 1

11

j jj

j j

j ML

OPTj

j ML

p C

A

N AANN

AN

Gaussian prior w adaptive hyperparameter

Discontinuity: Parameter is pruned atLow signal-to-noise Hansen & Rasmussen, Neural Comp (1994)Tipping “Relevance vector machine” (1999)

θ2A is a signal-to-noise measure

θML is maximum lik. opt.



Hyperparameter dynamics

Hyperparameters dynamically updated implies pruning

Pruning decisions based on SNR

Mechanism for cognitive selection, attention?



Hansen & Rasmussen, Neural Comp (1994)



Approximations needed for posteriors Approximations using asymptotic expansions

(Laplace etc) -JL Approximation of posteriors using tractable

(factorized) pdf’s by KL-fitting… Approximation of products using EP -AH Wednesday Approximation by MCMC –OWI Thursday



P. Højen-Sørensen: Thesis (2001)

Illustration of approximation by a gaussian pdf



Variational Bayes

Notation are observables and hidden variables – we analyse the log likelihood of a mixture model

xlog ( | ) log p( , , | )p M M d d

y y θ x θ x

,n ny x

p( , , | ) p( | , , )p( | , )p( | )M M M My θ x y x θ x θ θ



r( )

q( )

q( ) exp log ( , , | )

r( ) exp log ( , , | )

p M

p M

θ

x

x θ x y

θ θ x y

Variational Bayes:



Conjugate exponential families

1

( , | , ) , ) ( )exp[ ( , )]

( ) ( , ) ( ) exp( )

( , | , ) ( ) ( ', ') ( ) exp[ ( ( , )]

' 1

' ( , )

p M g

p h g

p M p h g

y x θ y x θ u y x

θ ν θ θ ν

y x θ θ ν θ θ ν u y x

ν ν u y x



Mini exercise What are the natural parameters for a Gaussian? What are the natural parameters for a MoG?



•Observation model and “Bayes factor”



•“Normal inverse gamma” prior – the conjugate prior for the GLM observation model



•Bayes factor is the ratio between normalization const. of NIG’s:



Exercises

Matthew Beal’s Mixture of Factor Analyzers code– Code available (variational-bayes.org)

Code a VB version of the BGML for signal detection– Code available for exact posterior

Variational Bayes 101

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