Variational Inference and Message Passing: Robotics & Vision Example

1. Variational Inference and Variational Message Passing John WinnMicrosoft Research, Cambridge 12th November 2004 Robotics Research Group, University of Oxford

2. Overview • Probabilistic models & Bayesian inference • Variational Inference • Variational Message Passing • Vision example

3. Overview • Probabilistic models & Bayesian inference • Variational Inference • Variational Message Passing • Vision example

4. Object class C P(C) Surface colour Lighting colour L S P(L) P(S|C) Image colour I P(I|L,S) Bayesian networks • Directed graph • Nodes represent variables • Links show dependencies • Conditional distributions at each node • Defines a joint distribution: P(C,L,S,I)=P(L) P(C) P(S|C) P(I|L,S)

5. Bayesian inference Object class C Observed variables V and hidden variables H. Hidden Surface colour Lighting colour Hidden variables includeparameters and latent variables. L S Learning/inference involves finding: Image colour I P(H1, H2…| V) Observed

6. Bayesian inference vs. ML/MAP • Consider learning one parameter θ • How should we represent this posterior distribution?

7. Bayesian inference vs. ML/MAP • Consider learning one parameter θ Maximum of P(V| θ) P(θ) P(V| θ) P(θ) θ θMAP

8. High probability mass Bayesian inference vs. ML/MAP • Consider learning one parameter θ High probability density P(V| θ) P(θ) θ θMAP

9. Bayesian inference vs. ML/MAP • Consider learning one parameter θ P(V| θ) P(θ) θ θML Samples

10. Bayesian inference vs. ML/MAP • Consider learning one parameter θ P(V| θ) P(θ) θ Variational approximation θML

11. Overview • Probabilistic models & Bayesian inference • Variational Inference • Variational Message Passing • Vision example

12. Variational Inference (in three easy steps…) • Choose a family of variational distributions Q(H). • Use Kullback-Leibler divergence KL(Q||P) as a measure of ‘distance’ between P(H|V) and Q(H). • Find Q which minimises divergence.

13. Q Minimising KL(P||Q) Q P KL Divergence Exclusive Minimising KL(Q||P) P Inclusive

14. Minimising the KL divergence • For arbitrary Q(H) fixed maximise minimise where • We choose a family of Q distributions where L(Q) is tractable to compute.

15. Minimising the KL divergence KL(Q || P) maximise ln P(V) fixed L(Q)

16. Minimising the KL divergence KL(Q || P) maximise ln P(V) fixed L(Q)

17. Minimising the KL divergence KL(Q || P) maximise ln P(V) fixed L(Q)

18. Minimising the KL divergence KL(Q || P) maximise ln P(V) fixed L(Q)

19. Minimising the KL divergence KL(Q || P) maximise ln P(V) fixed L(Q)

20. Optimal solution for one factor given by Factorised Approximation • Assume Q factorises No further assumptions are required!

21. Example: Univariate Gaussian • Likelihood function • Conjugate prior • Factorized variational distribution

22. Initial Configuration γ μ

23. After Updating Q(μ) γ μ

24. After Updating Q(γ) γ μ

25. Converged Solution γ μ

26. Lower Bound for GMM

27. Variational Equations for GMM

28. Overview • Probabilistic models & Bayesian inference • Variational Inference • Variational Message Passing • Vision example

29. Variational Message Passing • VMP makes it easier and quicker to apply factorised variational inference. • VMP carries out variational inference using local computations and message passing on the graphical model. • Modular algorithm allows modifying, extending or combining models.

30. Local Updates For factorised Q, update for each factor depends only on the Markov blanket: Updates can be carried out locally at each node.

31. For example, the Gaussian distribution T mg é ù é ù g X m g = + - gm + 2 ln P ( X | , ) ln 0 1 1 ê ú ê ú 2 2 - g p 2 / 2 X 2 ë û ë û VMP I: The Exponential Family • Conditional distributions expressed in exponential family form. = + + T ln P ( X | θ ) θ u ( X ) g ( θ ) f ( X ) ‘natural’ parameter vector sufficient statistics vector

32. = + + T ln P ( X | θ ) θ u ( X ) g ( θ ) f ( X ) = + + T ln P ( Z | X , Y ) φ ( Y , Z ) u ( X ) g ' ( X ) f ' ( Y , Z ) VMP II: Conjugacy • Parents and children are chosen to be conjugate i.e. same functional form X Y same Z • Examples: • Gaussian for the mean of a Gaussian • Gamma for the precision of a Gaussian • Dirichlet for the parameters of a discrete distribution

33. = + + T ln P ( X | θ ) θ u ( X ) g ( θ ) f ( X ) • Parent to child (X→Z) = + + T ln P ( Z | X , Y ) φ ( Y , Z ) u ( X ) g ' ( X ) f ' ( Y , Z ) • Child to parent (Z→X) VMP III: Messages • Conditionals • Messages X Y Z

34. VMP IV: Update • Optimal Q(X) has same form as P(X|θ) but with updated parameter vector θ* Computed from messages from parents • These messages can also be used to calculate the bound on the evidence L(Q) – see Winn & Bishop, 2004.

35. VMP Example • Learning parameters of a Gaussian from N data points. γ μ mean precision (inverse variance) x data N

36. VMP Example Message from γ to all x. γ μ need initialQ(γ) x N

37. VMP Example Messages from each xnto μ. γ μ x N Update Q(μ) parameter vector

38. VMP Example Message from updated μ to all x. γ μ x N

39. VMP Example Messages from each xnto γ. γ μ x N Update Q(γ) parameter vector

40. Features of VMP • Graph does not need to be a tree – it can contain loops (but not cycles). • Flexible message passing schedule – factors can be updated in any order. • Distributions can be discrete or continuous, multivariate, truncated (e.g. rectified Gaussian). • Can have deterministic relationships (A=B+C). • Allows for point estimates e.g. of hyper-parameters

41. VMP Software: VIBES Free download from vibes.sourceforge.net

42. Overview • Probabilistic models & Bayesian inference • Variational Inference • Variational Message Passing • Vision example

43. Flexible sprite model Proposed by Jojic & Frey (2001) Set of images e.g. frames from a video x N

44. Flexible sprite model f π Sprite appearance and shape x N

45. Flexible sprite model f π Sprite transform for this image (discretised) T m x Mask for this image N

46. Flexible sprite model b f π Background T m Noise β x N

47. VMP π f b T m β x N Winn & Blake (NIPS 2004)

48. Results of VMP on hand video Original video Learned appearance and mask Learned transforms (i.e. motion)

49. Conclusions • Variational Message Passing allows approximate Bayesian inference for a wide range of models. • VMP simplifies the construction, testing, extension and comparison of models. • You can try VMP for yourself vibes.sourceforge.net

50. That’s all folks!