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CS188: Computational Models of Human Behavior
Introduction to graphical models slide Credits: Kevin Murphy, mark pashkin, zoubin ghahramani and jeff bilmes
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Reasoning under uncertainty
In many settings, we need to understand what is going on in a system when we have imperfect or incomplete information For example, we might deploy a burglar alarm to detect intruders But the sensor could be triggered by other events, e.g., earth-quake Probabilities quantify the uncertainties regarding the occurrence of events
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Probability spaces A probability space represents our uncertainty regarding an experiment It has two parts: A sample space , which is the set of outcomes the probability measure P, which is a real function of the subsets of A set of outcomes A is called an event. P(A) represents how likely it is that the experiment’s actual outcome be a member of A
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An example If our experiment is to deploy a burglar alarm and see if it works, then there could be four outcomes: = {(alarm, intruder), (no alarm, intruder), (alarm, no intruder), (no alarm, no intruder)} Our choice of P has to obey these simple rules …
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The three axioms of probability theory
P(A)≥0 for all events A P()=1 P(A U B) = P(A) + P(B) for disjoint events A and B
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Some consequences of the axioms
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Example Let’s assign a probability to each outcome ω
These probabilities must be non-negative and sum to one intruder no intruder alarm 0.002 0.003 no alarm 0.001 0.994
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Conditional Probability
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Marginal probability Marginal probability is then the unconditional probability P(A) of the event A; that is, the probability of A, regardless of whether event B did or did not occur. For example, if there are two possible outcomes corresponding to events B and B', this means that P(A) = P(AB) + P(AB’) This is called marginalization
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Example If P is defined by then
P({(intruder, alarm)|(intruder, alarm),(no intruder, alarm)}) intruder no intruder alarm 0.002 0.003 no alarm 0.001 0.994
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The product rule The probability that A and B both happen is the probability that A happens and B happens, given A has occurred
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The chain rule Applying the product rule repeatedly:
P(A1,A2,…,Ak) = P(A1) P(A2|A1)P(A3|A2,A1)…P(Ak|Ak-1,…,A1) Where P(A3|A2,A1) = P(A3|A2A1)
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Bayes’ rule Use the product rule both ways with P(AB)
P(A B) = P(A)P(B|A) P(A B) = P(B)P(A|B)
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Random variables and densities
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Inference One of the central problems of computational probability theory Many problems can be formulated in these terms. Examples: The probability that there is an intruder given the alarm went off is pI|A(true, true) Inference requires manipulating densities
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Probabilistic graphical models
Combination of graph theory and probability theory Graph structure specifies which parts of the system are directly dependent Local functions at each node specify how different parts interaction Bayesian Networks = Probabilistic Graphical Models based on directed acyclic graph Markov Networks = Probabilistic Graphical Models based on undirected graph
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Some broad questions
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Bayesian Networks Nodes are random variables
Edges represent dependence – no directed cycles allowed) P(X1:N) = P(X1)P(X2|X1)P(X3|X1,X2) = P(Xi|X1:i-1) = P(Xi|Xi) x2 x3 x5 x4 x7 x6 x1
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Example Water sprinkler Bayes net
P(C,S,R,W)=P(C)P(S|C)P(R|C,S)P(W|C,S,R) chain rule =P(C)P(S|C)P(R|C)P(W|C,S,R) since R S|C =P(C)P(S|C)P(R|C)P(W|S,R) since W C|R,S
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Inference
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Naïve inference
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Problem with naïve representation of the joint probability
Problems with the working with the joint probability Representation: big table of numbers is hard to understand Inference: computing a marginal P(Xi) takes O(2N) time Learning: there are O(2N) parameters to estimate Graphical models solve the above problems by providing a structured representation for the joint Graphs encode conditional independence properties and represent families of probability distribution that satisfy these properties
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Bayesian networks provide a compact representation of the joint probability
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Conditional probabilities
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Another example: medical diagnosis (classification)
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Approach: build a Bayes’ net and use Bayes’s rule to get class probability
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A very simple Bayes’ net: Naïve Bayes
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Naïve Bayes classifier for medical diagnosis
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Another commonly used Bayes’ net: Hidden Markov Model (HMM)
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Conditional independence properties of Bayesian networks: chains
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Conditional independence properties of Bayesian networks: common cause
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Conditional independence properties of Bayesian networks: explaining away
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Global Markov properties of DAGs
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Bayes ball algorithm
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Example
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Undirected graphical models
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Parameterization
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Clique potentials
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Interpretation of clique potentials
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Examples
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Joint distribution of an undirected graphical model
Complexity scales exponentially as 2n for binary random variable if we use a naïve approach to computing the partition function
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Max clique vs. sub-clique
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Log-linear models
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Log-linear models
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Log-linear models
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Summary
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Summary
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From directed to undirected graphs
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From directed to undirected graphs
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Example of moralization
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Comparing directed and undirected models
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Expressive power w x y z x y z
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Coming back to inference
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Coming back to inference
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Belief propagation in trees
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Learning
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Parameter Estimation
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Parameter Estimation
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Maximum-likelihood Estimation (MLE)
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Example: 1-D Gaussian
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MLE for Bayes’ Net
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MLE for Bayes’ Net
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MLE for Bayes’ Net with Discrete Nodes
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Parameter Estimation with Hidden Nodes
Z Z Z Z Z Z Z6
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Why is learning harder?
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Where do hidden variables come from?
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Parameter Estimation with Hidden Nodes
z z
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EM
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Different Learning Conditions
Structure Observability Full Partial Known Closed form search EM Unknown Local search Structural EM
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