COMP90051 Statistical Machine Learning
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1 COMP90051 Statistical Machine Learning Semester 2, 2016 Lecturer: Trevor Cohn 20. PGM Representation
2 Next Lectures Representation of joint distributions Conditional/marginal independence * Directed vs undirected Probabilistic inference * Computing other distributions from joint Statistical inference * Learn parameters from (missing) data 2
3 Probabilistic Graphical Models Marriage of graph theory and probability theory. Tool of choice for Bayesian statistical learning. We ll stick with easier discrete case, ideas generalise to continuous. 3
4 Motivation by practical importance Many applications * Phylogenetic trees * Pedigrees, Linkage analysis * Error-control codes * Speech recognition * Document topic models * Probabilistic parsing * Image segmentation * Unifies many previouslydiscovered algorithms * HMMs * Kalman filters * Mixture models * LDA * MRFs * CRF * Logistic, linear regression * 4
5 Motivation by way of comparison Bayesian statistical learning Model joint distribution of X s,y and parameter r.v. s * Priors : marginals on parameters Training: update prior to posterior using observed data Prediction: output posterior, or some function of it (MAP) PGMs aka Bayes Nets Efficient joint representation * Independence made explicit * Trade-off between expressiveness and need for data, easy to make * Easy for practitioners to model Algorithms to fit parameters, compute marginals, posterior 5
6 Everything Starts at the Joint Distribution All joint distributions on discrete r.v. s can be represented as tables #rows grows exponentially with #r.v. s Example: Truth Tables * M Boolean r.v. s require 2 M -1 rows * Table assigns probability per row A B C Prob ? A B 0.05 C 6
7 The Good: What we can do with the joint Probabilistic inference from joint on r.v. s * Computing any other distributions involving our r.v. s Pattern: want a distribution, have joint; use: Bayes rule + marginalisation Example: naïve Bayes classifier * Predict class y of instance x by maximising Pr Y = y X = x = )* +,-,X,x )* (X,x) = )* +,-,X,x 4 )* X,x,+,- Recall: integration (over parameters) continuous equivalent of sum (both referred to as marginalisation) 7
8 The Bad & Ugly: Tables waaaaay too large!! The Bad: Computational complexity * Tables have exponential number of rows in number of r.v. s * Therefore à poor space & time to marginalise The Ugly: Model complexity * Way too flexible * Way too many parameters to fit à need lots of data OR will overfit Antidote: assume independence! A B C Prob ? 8
9 Example: You re late! Modeling a tardy lecturer. Boolean r.v. s * T: Trevor teaches the class * S: It is sunny (o.w. bad weather) * L: The lecturer arrives late (o.w. on time) Assume: Trevor sometimes delayed by bad weather, Trevor more likely late than co-lecturer * Pr S T = Pr S, Pr S = 0.3, Pr T = 0.6 Lateness not independent on weather, lecturer * Need Pr L T = t, S = s for all combinations Need just 6 parameters Pr L = true T False True False S True
10 Independence: not a dirty word Lazy Lecturer Model Model details # params Our model with S, T independence Pr S, T factors to Pr S Pr T 2 Pr L T, S modelled in full 4 Assumption-free model Pr L, T, S modelled in full 7 Independence assumptions * Can be reasonable in light of domain expertise * Allow us to factor à Key to tractable models 10
11 Factoring Joint Distributions Chain Rule: for any ordering of r.v. s can always factor: Pr X C, X D,, X E = F Pr X G X GHC,, X E Model s independence assumptions correspond to Dropping conditioning r.v. s in the factors! Example unconditional indep.: Pr X C X D = Pr X C Example conditional indep.: Pr X C X D, X I = Pr X C X D E Example: independent r.v. s Pr X C,, X E = G,C Pr X G E G,C Simpler factors speed inference and avoid overfitting 11
12 Directed PGM Nodes Edges (acyclic) Random variables Conditional dependence * Node table: Pr child parents * Child directly depends on parents Joint factorisation E Pr X C, X D,, X E = G,C Pr X G X S parents(x G ) Tardy Lecturer Example S T Pr S Pr T L Pr L S, T 12
13 Example: Nuclear power plant Core temperature à Temperature Gauge à Alarm Model uncertainty in monitoring failure * GRL: gauge reads low * CTL: core temperature low * FG: faulty gauge * FA: faulty alarm * AS: alarm sounds PGMs to the rescue! Pr(FA) Pr(CTL) FA CTL AS GRL Pr AS GRL, FA) Joint Pr(CTL, FG, FA, GRL, AS) given by FG Pr(FG) Pr GRL CTL, FG) Pr(AS FA, GRL) Pr(FA) Pr(GRL CTL, FG) Pr(CTL) Pr(FG) 13
14 Naïve Bayes Y Y ~ Bernoulli θ Aside: Bernoulli is just Binomial with count=1 X 1 X d X S Y ~ Bernoulli θ S,+ Pr Y, X C,, X c = Pr X C,, X c, Y = Pr X C Y Pr X D X C, Y Pr X c X C,, X cdc, Y Pr Y = Pr X C Y Pr X D Y Pr X c Y Pr Y Prediction: predict label maximising Pr Y X C,, X c 14
15 Short-hand for repeats: Plate notation X 1 X d = X i i=1..d Y Y = X 1 X d X i i=1..d 15
16 PGMs frequentist OR Bayesian PGMs represent joints, which are central to Bayesian Catch is that Bayesians add: node per parameters, with table being the parameter s prior θ Y Y ~ Bernoulli θ θ C,e θ C,C θ c,e θ c,c X 1 X d X S Y ~ Bernoulli θ S,+ θ f s ~ Beta 16
17 Example PGMs The hidden Markov model (HMM); lattice Markov random field (MRF) 17
18 The HMM (and Kalman Filter) Sequential observed outputs from hidden state The Kalman filter same with continuous Gaussian r.v. s A CRF is the undirected analogue o C q C o D q D o I q I o i q i 18
19 HMM Applications NLP part of speech tagging: given words in sentence, infer hidden parts of speech I love Machine Learning à noun, verb, noun, noun Speech recognition: given waveform, determine phonemes Biological sequences: classification, search, alignment Computer vision: identify who s walking in video, tracking 19
20 Fundamental HMM Tasks HMM Task Evaluation. Given an HMM μ and observation sequence O, determine likelihood Pr (O μ) Decoding. Given an HMM μ and observation sequence O, determine most probable hidden state sequence Q Learning. Given an observation sequence O and set of states, learn parameters A, B, Π PGM Task Probabilistic inference MAP point estimate Statistical inference 20
21 Computer Vision Hidden square-lattice Markov random fields 21
22 Pixel labelling tasks in Computer Vision Semantic labelling (Gould et al. 09) Interactive figure-ground segmentation (Boykov & Jolly 2011) Denoising (Felzenszwalb & Huttenlocher 04) 22
23 What these tasks have in common Hidden state representing semantics of image * Semantic labelling: Cow vs. tree vs. grass vs. sky vs. house * Fore-back segment: Figure vs. ground * Denoising: Clean pixels Pixels of image * What we observe of hidden state Remind you of HMMs? 23
24 A hidden square-lattice MRF Hidden states: square-lattice model * Boolean for two-class states * Discrete for multi-class * Continuous for denoising Pixels: observed outputs * Continuous e.g. Normal o GS q GS 24
25 Application to sequences: CRFs Conditional Random Field: Same model applied to sequences * observed outputs are words, speech, amino acids etc * states are tags: part-of-speech, phone, alignment CRFs are discriminative, model P(Q O) * versus HMM s which are generative, P(Q,O) * undirected PGM more general and expressive o C o D o I o i q C q D q I q i 25
26 Summary Probabilistic graphical models * Motivation: applications, unifies algorithms * Motivation: ideal tool for Bayesians * Independence lowers computational/model complexity * PGMs: compact representation of factorised joints 26
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