Approximate Inference Part 1 of 2

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1 Approximate Inference Part 1 of 2 Tom Minka Microsoft Research, Cambridge, UK Machine Learning Summer School

2 Bayesian paradigm Consistent use of probability theory for representing unknowns (parameters, latent variables, missing data) 2

3 Bayesian paradigm Bayesian posterior distribution summarizes what we ve learned from training data and prior knowledge Can use posterior to: Describe training data Make predictions on test data Incorporate new data (online learning) Today s question: How to efficiently represent and compute posteriors? 3

4 Factor graphs Shows how a function of several variables can be factored into a product of simpler functions f(x,y,z) = (x+y)(y+z)(x+z) Very useful for representing posteriors 4

5 Example factor graph p( x m) N( x ; m,1) i = i 5

6 Two tasks Modeling What graph should I use for this data? Inference Given the graph and data, what is the mean of x (for example)? Algorithms: Sampling Variable elimination Message-passing (Expectation Propagation, Variational Bayes, ) 6

7 A (seemingly) intractable problem 7

8 Clutter problem Want to estimate x given multiple y s 8

9 Exact posterior exact p(x,d D) x 9

10 Representing posterior distributions Sampling Deterministic approximation Good for complex, multi-modal distributions Slow, but predictable accuracy Good for simple, smooth distributions Fast, but unpredictable accuracy 10

11 Deterministic approximation Laplace s method Bayesian curve fitting, neural networks (MacKay) Bayesian PCA (Minka) Variational bounds Bayesian mixture of experts (Waterhouse) Mixtures of PCA (Tipping, Bishop) Factorial/coupled Markov models (Ghahramani, Jordan, Williams) 11

12 Moment matching Another way to perform deterministic approximation Much higher accuracy on some problems Assumed-density filtering (1984) Loopy belief propagation (1997) Expectation Propagation (2001) 12

13 Today Moment matching (Expectation Propagation) Tomorrow Variational bounds (Variational Message Passing) 13

14 Best Gaussian by moment matching exact bestgaussian p(x,d) ) x 14

15 Strategy Approximate each factor by a Gaussian in x 15

16 Approximating a single factor 16

17 (naïve) f i (x) \ q i ( x ) = p(x) 17

18 (informed) f i (x) \ q i ( x ) = p(x) 18

19 Single factor with Gaussian context 19

20 Gaussian multiplication formula 20

21 Approximation with narrow context 21

22 Approximation with medium context 22

23 Approximation with wide context 23

24 Two factors x x Message passing 24

25 Three factors x x Message passing 25

26 Message Passing = Distributed Optimization Messages represent a simpler distribution q(x) that approximates p(x) A distributed representation Message passing = optimizing q to fit p q stands in for p when answering queries Choices: What type of distribution to construct (approximating family) What cost to minimize (divergence measure) 26

27 Distributed divergence minimization Write p as product of factors: Approximate factors one by one: Multiply to get the approximation: 27

28 Gaussian found by EP ep exact bestgaussian p( (x,d) x 28

29 Other methods vb laplace exact p( (x,d) x 29

30 Accuracy Posterior mean: exact = ep = laplace = vb = Posterior variance: exact = ep = laplace = vb =

31 Cost vs. accuracy 20 points 200 points Deterministic methods improve with more data (posterior is more Gaussian) Sampling methods do not 31

32 Censoring example Want to estimate x given multiple y s p ( x) = N( x;0,100) p( y i x) = N( y; x,1) p( y i > t x) = t N( y; x,1) dy + t N( y; x,1) dy 32

33 Time series problems 33

34 Example: Tracking Guess the position of an object given noisy measurements y 2 x 2 x 1 x 3 y 4 y 1 y 3 x 4 Object 34

35 Factor graph x1 x x 2 3 x4 y1 y2 y3 y4 e.g. t = xt 1 x + ν t (random walk) y t = x t + noise want distribution of x s given y s 35

36 Approximate factor graph x1 x x 2 3 x4 36

37 Splitting a pairwise factor x1 x2 x1 x2 37

38 Splitting in context x x 2 3 x x

39 Sweeping through the graph x1 x x 2 3 x4 39

40 Sweeping through the graph x1 x x 2 3 x4 40

41 Sweeping through the graph x1 x x 2 3 x4 41

42 Sweeping through the graph x1 x x 2 3 x4 42

43 Example: Poisson tracking y t is a Poisson-distributed integer with mean exp(x t ) 43

44 Poisson tracking model p( x 1 ) ~ N(0,100) p( x x ) ~ N ( x ( t t 1 t 1,0.01) xt p( yt xt ) = exp( yt xt e ) / yt! 44

45 Factor graph x1 x x 2 3 x4 y1 y2 y3 y4 x1 x x 2 3 x4 45

46 Approximating a measurement factor x 1 y 1 x 1 46

47 47

48 Posterior for the last state 48

49 49

50 50

51 EP for signal detection Wireless communication problem Transmitted signal = a sin( ω t + φ) ( a, φ) vary to encode each symbol In complex numbers: iφ ae (Qi and Minka, 2003) Im a φ Re 51

52 Binary symbols, Gaussian noise Symbols are 1 0 s =1 and s = 1 (in complex plane) Received signal = a sin( ωt + φ) + noise y t Optimal detection is easy in this case y t 0 s 1 s 52

53 Fading channel Channel systematically changes amplitude and phase: y x s + noise = t t s t = transmitted symbol x t t = channel multiplier (complex number) x t changes over time yt 1 x t s 0 x t s 53

54 Differential detection Use last measurement to estimate state: x t y s / t 1 t 1 State estimate is noisy can we do better? y t y t 1 y t 1 54

55 Factor graph s1 s2 s3 s4 y1 y2 y y 3 4 x1 x x 2 3 x4 Symbols can also be correlated (e.g. error-correcting code) Channel dynamics are learned from training data (all 1 s) 55

56 56

57 57

58 Splitting a transition factor 58

59 Splitting a measurement factor 59

60 On-line implementation Iterate over the last δ measurements Previous measurements act as prior Results comparable to particle filtering, but much faster 60

61 61

62 Classification problems 62

63 Spam filtering by linear separation Not spam Spam Choose a boundary that will generalize to new data 63

64 Linear separation Minimum training error solution (Perceptron) Too arbitrary won t generalize well 64

65 Linear separation Maximum-margin solution (SVM) Ignores information in the vertical direction 65

66 Linear separation Bayesian solution (via averaging) Has a margin, and uses information in all dimensions 66

67 Geometry of linear separation Separator is any vector w such that: w T xi > 0 (class 1) w T x < 0 (class 2) w i =1 (sphere) This set has an unusual shape SVM: Optimize over it Bayes: Average over it 67

68 Performance on linear separation EP Gaussian approximation to posterior 68

69 Factor graph p ( w) = N( w; 0, I) 69

70 Computing moments q ( w ) = N( w; m, V \ i \ i \ i ) 70

71 Computing moments 71

72 Time vs. accuracy A typical run on the 3-point problem Error = distance to true mean of w Billiard = Monte Carlo sampling (Herbrich et al, 2001) Opper&Winther s algorithms: MF = mean-field theory TAP = cavity method (equiv to Gaussian EP for this problem) 72

73 Gaussian kernels Map data into high-dimensional space so that 73

74 Bayesian model comparison Multiple models M i with prior probabilities p(m i ) Posterior probabilities: For equal priors, models are compared using model evidence: 74

75 Highest-probability kernel 75

76 Margin-maximizing kernel 76

77 Bayesian feature selection Synthetic data where 6 features are relevant (out of 20) Bayes picks 6 Margin picks 13 77

78 EP versus Monte Carlo Monte Carlo is general but expensive A sledgehammer EP exploits underlying simplicity of the problem (if it exists) Monte Carlo is still needed for complex problems (e.g. large isolated peaks) Trick is to know what problem you have 78

79 Software for EP Bayes Point Machine toolbox Sparse Online Gaussian Process toolbox Infer.NET 79

80 Further reading EP bibliography EP quick reference 80

81 Tomorrow Variational Message Passing Divergence measures Comparisons to EP 81

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