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Variational Methods for Graphical Models Micheal I. Jordan Zoubin Ghahramani Tommi S. Jaakkola Lawrence K. Saul Presented by: Afsaneh Shirazi

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2 Outline Motivation Inference in graphical models Exact inference is intractable Variational methodology –Sequential approach –Block approach Conclusions

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3 Motivation (Example: Medical Diagnosis) symptoms diseases What is the most probable disease?

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4 Motivation We want to answer some queries about our data Graphical model is a way to model data Inference in some graphical models is intractable (NP-hard) Variational methods simplify the inference in graphical models by using approximation

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5 Graphical Models Directed (Bayesian network) Undirected S1S1 S3S3 S5S5 S4S4 S2S2 P(S 2 ) P(S 1 ) P(S 5 |S 3,S 4 ) P(S 3 |S 1,S 2 ) P(S 4 |S 3 ) (C 1 ) (C 2 ) (C 3 )

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6 Inference in Graphical Models Inference: Given a graphical model, the process of computing answers to queries How computationally hard is this decision problem? Theorem: Computing P(X = x) in a Bayesian network is NP-hard

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7 Why Exact Inference is Intractable? symptoms diseases Diagnose the most probable disease

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8 Why Exact Inference is Intractable? symptoms diseases : Observed symptoms

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9 Why Exact Inference is Intractable? symptoms diseases :Noisy-OR model 101

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10 Why Exact Inference is Intractable? symptoms diseases : Noisy-OR model 101

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11 Why Exact Inference is Intractable?

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12 Why Exact Inference is Intractable? symptoms diseases : Observed symptoms

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13 Why Exact Inference is Intractable? symptoms diseases : Observed symptoms

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14 Reducing the Computational Complexity Variational Methods Simple graph for exact methods Approximate the probability distribution Use the role of convexity

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15 Express a Function Variationally is a concave function

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16 Express a Function Variationally is a concave function

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17 Express a Function Variationally If the function is not convex or concave: transform the function to a desired form Example: logistic function Transformation Approximation Transforming back

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18 Approaches to Variational Methods Sequential Approach: (on-line) nodes are transformed in an order, determined during inference process Block Approach: (off-line) has obvious substructures

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19 Sequential Approach (Two Methods) Untransformed Graph Transform one node at a time Simple Graph for exact methods Reintroduce one node at a time Simple Graph for exact methods Completely transformed Graph

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20 Sequential Approach (Example) symptoms diseases Log Concave

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21 Sequential Approach (Example) symptoms diseases Log Concave

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22 Sequential Approach (Example) symptoms diseases 1

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23 Sequential Approach (Example) symptoms diseases 1

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24 Sequential Approach (Example) symptoms diseases 1

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25 Sequential Approach (Upper Bound and Lower Bound) We need both lower bound and upper bound

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26 How to Compute Lower Bound for a Concave Function? Lower bound for concave functions: Variational parameter is probability distribution

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27 Block Approach (Overview) Off-line application of sequential approach –Identify some structure amenable to exact inference –Family of probability distribution via introduction of parameters –Choose best approximation based on evidence

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28 Block Approach (Details) KL divergence Family of Minimize KL divergence

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29 Block Approach (Example – Boltzmann machine) SiSi SjSj

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30 Block Approach (Example – Boltzmann machine) SiSi S j =1

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31 Block Approach (Example – Boltzmann machine) sisi sjsj

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32 Block Approach (Example – Boltzmann machine) sisi sjsj Minimize KL Divergence

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33 Block Approach (Example – Boltzmann machine) sisi sjsj Minimize KL Divergence Mean field equations: solve for fixed point

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34 Conclusions Time or space complexity of exact calculation is unacceptable Complex graphs can be probabilistically simple Inference in simplified models provides bounds on probabilities in the original model

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36 Extra Slides

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37 Concerns Approximation accuracy Strong dependencies can be identified Not based on convexity transformation Not able to assure that the framework will transfer to other examples Not straightforward to develop a variational approximation for new architectures

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38 Justification for KL Divergence Best lower bound on the probability of the evidence

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39 EM Maximum likelihood parameter estimation: Following function is the lower bound on log likelihood KL Divergence between Q(H|E) and P(H|E, )

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40 EM 1.Maximize the bound with respect to Q 2.Fix Q, maximize with respect to Traditional EM Approximation to EM algorithm

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41 Principle of Inference DAG Junction Tree Inconsistent Junction Tree Initialization Consistent Junction Tree Propagation Marginalization

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42 Example: Create Join Tree X1X2 Y1Y2 HMM with 2 time steps: Junction Tree: X1,X2 X1,Y1 X2,Y2 X1 X2

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43 Example: Initialization Variable Associated Cluster Potential function X1X1,Y1 Y1X1,Y1 X2X1,X2 Y2X2,Y2 X1,X2 X1,Y1 X2,Y2 X1 X2

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44 Example: Collect Evidence Choose arbitrary clique, e.g. X1,X2, where all potential functions will be collected. Call recursively neighboring cliques for messages: 1. Call X1,Y1. –1. Projection: –2. Absorption:

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45 Example: Collect Evidence (cont.) 2. Call X2,Y2: –1. Projection: –2. Absorption: X1,X2 X1,Y1 X2,Y2 X1 X2

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46 Example: Distribute Evidence Pass messages recursively to neighboring nodes Pass message from X1,X2 to X1,Y1: –1. Projection: –2. Absorption:

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47 Example: Distribute Evidence (cont.) Pass message from X1,X2 to X2,Y2: –1. Projection: –2. Absorption: X1,X2 X1,Y1 X2,Y2 X1 X2

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48 Example: Inference with evidence Assume we want to compute: P(X2|Y1=0,Y2=1) (state estimation) Assign likelihoods to the potential functions during initialization:

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49 Example: Inference with evidence (cont.) Repeating the same steps as in the previous case, we obtain:

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50 Variable Elimination General idea: Write query in the form Iteratively –Move all irrelevant terms outside of innermost sum –Perform innermost sum, getting a new term –Insert the new term into the product

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51 Complexity of variable elimination Suppose in one elimination step we compute This requires multiplications additions Complexity is exponential in number of variables in the intermediate factor

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52 Chordal Graphs elimination ordering undirected chordal graph Graph: Maximal cliques are factors in elimination Factors in elimination are cliques in the graph Complexity is exponential in size of the largest clique in graph L T A B X V S D V S L T A B XD

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53 Induced Width The size of the largest clique in the induced graph is thus an indicator for the complexity of variable elimination This quantity is called the induced width of a graph according to the specified ordering Finding a good ordering for a graph is equivalent to finding the minimal induced width of the graph

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54 Properties of Junction Trees In every junction tree: –For each cluster (or sepset), –The probability distribution of any variable, using any cluster (or sepset) that contains

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55 Exact inference Using Junction Trees Undirected tree Each node is a cluster Running intersection property: –Given two clusters and, all clusters on the path between and contain Separator sets (sepsets): –Intersection of adjacent clusters ADEABD DEF ADDE Cluster ABD Sepset DE

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56 Constructing Junction Trees Marrying Parents X4X4 X6X6 X5X5 X3X3 X2X2 X1X1

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57 Moral Graph X4X4 X6X6 X5X5 X3X3 X2X2 X1X1

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58 Triangulation X4X4 X6X6 X5X5 X3X3 X2X2 X1X1

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59 Identify Cliques X4X4 X6X6 X5X5 X3X3 X2X2 X1X1 X2X5X6X2X5X6 X1X2X3X1X2X3 X2X3X5X2X3X5 X2X4X2X4

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60 Junction Tree Junction tree is a subgraph of the clique graph satisfying the running intersection property X1X2X3X1X2X3 X2X5X6X2X5X6 X2X3X5X2X3X5 X2X3X2X3 X2X5X2X5 X2X2 X2X5X6X2X5X6 X2X4X2X4 X1X2X3X1X2X3 X2X3X5X2X3X5 X2X4X2X4

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61 Constructing Junction Trees DAG Moral GraphTriangulated GraphJunction TreeIdentify Cliques

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62 Sequential Approach (Example) Lower bound for medical diagnosis ex:

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. Exact Inference in Bayesian Networks Lecture 9.

. Exact Inference in Bayesian Networks Lecture 9.

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