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Probabilistic Reasoning Bayesian Belief Networks Constructing Bayesian Networks Representing Conditional Distributions Summary

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Bayesian Belief Networks (BBN) A Bayesian Belief Network is a method to describe the joint probability distribution of a set of variables. Let x1, x2, …, xn be a set of random variables. A Bayesian Belief Network or BBN will tell us the probability of any combination of x1, x2,.., xn.

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Representation A BBN represents the joint probability distribution of a set of variables by explicitly indicating the assumptions of conditional independence through the following: a)Nodes representing random variables b)Directed links representing relations. c)Conditional probability distributions. d) The graph is a directed acyclic graph.

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Example 1 Weather Cavity Toothache Catch

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Example

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Representation Each variable is independent of its non-descendants given its predecessors. We say x1 is a descendant of x2 if there is a direct path from x2 to x1. Example: Predecessors of Alarm: Burglary, Earthquake.

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Joint Probability Distribution To compute the joint probability distribution of a set of variables given a Bayesian Belief Network we simply use the following formula: P(x1,x2,…,xn) = Π P(xi | Parents(xi)) Where parents are the immediate predecessors of xi.

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Joint Probability Distribution Example: P(John, Mary,Alarm,~Burglary,~Earthquake) : P(John|Alarm) P(Mary|Alarm) P(Alarm|~Burglary ^ ~Earthquake) P(~Burglary) P(~Earthquake) = 0.00062

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Conditional Probabilities Alarm Burglary Earthquake B E P(A) t t 0.95 t f 0.94 f t 0.29 f f 0.001

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Probabilistic Reasoning Bayesian Belief Networks Constructing Bayesian Networks Representing Conditional Distributions Summary

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Constructing Bayesian Networks Choose the right order from causes to effects. P(x1,x2,…,xn) = P(xn|xn-1,..,x1)P(xn-1,…,x1) = Π P(xi|xi-1,…,x1) -- chain rule Example: P(x1,x2,x3) = P(x1|x2,x3)P(x2|x3)P(x3)

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How to construct BBN P(x1,x2,x3) x3 x2 x1 root cause leaf Correct order: add root causes first, and then “leaves”, with no influence on other nodes.

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Compactness BBN are locally structured systems. They represent joint distributions compactly. Assume n random variables, each influenced by k nodes. Size BBN: n2 k Full size: 2 n

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Probabilistic Reasoning Bayesian Belief Networks Constructing Bayesian Networks Representing Conditional Distributions Summary

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Representing Conditional Distributions Even if k is small O(2 k ) may be unmanageable. Solution: use canonical distributions. Example: U.S. Canada Mexico North America simple disjunction

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Noisy-OR Cold Flu Malaria Fever A link may be inhibited due to uncertainty

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Noisy-OR Inhibitions probabilities: P(~fever | cold, ~flu, ~malaria) = 0.6 P(~fever | ~cold, flu, ~malaria) = 0.2 P(~fever | ~cold, ~flu, malaria) = 0.1

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Noisy-OR Now the whole probability can be built: P(~fever | cold, ~flu, malaria) = 0.6 x 0.1 P(~fever | cold, flu, ~malaria) = 0.6 x 0.2 P(~fever | ~cold, flu, malaria) = 0.2 x 0.1 P(~fever | cold, flu, malaria) = 0.6 x 0.2 x 0.1 P(~fever | ~cold, ~flu, ~malaria) = 1.0

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Continuous Variables Continuous variables can be discretized. Or define probability density functions Example: Gaussian distribution. A network with both variables is called a Hybrid Bayesian Network.

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Continuous Variables Subsidy Harvest Cost Buys

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Continuous Variables P(cost | harvest, subsidy) P(cost | harvest, ~subsidy) Normal distribution x P(x)

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Probabilistic Reasoning Bayesian Belief Networks Constructing Bayesian Networks Representing Conditional Distributions Summary

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Bayesian networks are directed acyclic graphs that concisely represent conditional independence relations among random variables. BBN specify the full joint probability distribution of a set of variables. BBN can by hybrid, combining categorical variables with numeric variables.

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Bayesian networks. Motivation We saw that the full joint probability can be used to answer any question about the domain, but can become intractable as.

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