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Uncertainty Powered By Docstoc
                FOR ENGINEERS
                    Lecture 16, 6/1/2005

                  University of Washington,
            Department of Electrical Engineering
                         Spring 2005
             Instructor: Professor Jeff A. Bilmes

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            Uncertainty & Bayesian
                  Chapter 13/14

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• Inference
• Independence and Bayes' Rule
• Chapter 14
     – Syntax
     – Semantics
     – Parameterized Distributions
     – Inference in Bayesian Networks

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                   On the final
• Same format as midterm
• closed book/closed notes
• Might test on all material of the quarter,
  including today (i.e., chapters 1-9, 13,14)
     – but will not test on fuzzy logic.
• Will be weighted towards latter half of the
  course though.

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• Last HW of the quarter
• Due next Wed, June 1st, in class:
     – Chapter 13: 13.3, 13.7, 13.16
     – Chapter 14: 14.2, 14.3, 14.10

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            Bayesian Networks

                Chapter 14

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                Bayesian networks
• A simple, graphical notation for conditional
  independence assertions and hence for compact
  specification of full joint distributions

• Syntax:
     – a set of nodes, one per variable
     – a directed, acyclic graph (link ≈ "directly influences")
     – a conditional distribution for each node given its parents:
                                 P (Xi | Parents (Xi))

• In the simplest case, conditional distribution represented
  as a conditional probability table (CPT) giving the
  distribution over Xi for each combination of parent values
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            Example contd.

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The full joint distribution is defined as the product of the local
  conditional distributions:

            P (X1, … ,Xn) = πi = 1 P (Xi | Parents(Xi))

e.g., P(j  m  a  b  e)

   = P (j | a) P (m | a) P (a | b, e) P (b) P (e)

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                Local Semantics
Local semantics: each node is conditionally independent of its
  nondescendants given its parents

Thm: Local semantics  global semantics

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            Example: car diagnosis
• Initial evidence: car won’t start
• Testable variables (green), “broken, so fix it” variables (orange)
• Hidden variables (gray) ensure sparse structure, reduce parameters.

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            Example: car insurance

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            compact conditional dists.
•   CPT grows exponentially with number of parents
•   CPT becomes infinite with continuous-valued parent or child
•   Solution: canonical distributions that are defined compactly
•   Deterministic nodes are the simplest case:
     – X = f(Parents(X)), for some deterministic function f (could be logical
• E.g., boolean functions
     – NorthAmerican  Canadian Ç US Ç Mexican
• E.g,. numerical relationships among continuous variables

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            compact conditional dists.
• “Noisy-Or” distributions model multiple interacting causes:
     –   1) Parents U1, …, Uk include all possible causes
     –   2) Independent failure probability qi for each cause alone
     –    : X ´ U1 Æ U2 Æ … Æ Uk
     –    P(X|U1, …, Uj, : Uj+1, …, : Uk ) = 1 - i=1j qi
• Number of parameters is linear in number of parents.

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     Hybrid (discrete+cont) networks
• Discrete (Subsidy? and Buys?); continuous (Harvest and Cost)

• Option 1: discretization – large errors and large CPTs
• Option 2: finitely parameterized canonical families
     – Gaussians, Logistic Distributions (as used in Neural Networks)
• Continuous variables, discrete+continuous parents (e.g., Cost)
• Discrete variables, continuous parents (e.g., Buys?)

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•   by enumeration
•   by variable elimination
•   by stochastic simulation
•   by Markov chain Monte Carlo

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                  Inference Tasks
• Simple queries: compute posterior marginal, P(Xi|E=e)
     – e.g., P(NoGas|Gague=empty,Lights=on,Starts=false)
• Conjunctive queries:
     – P(Xi,Xj|E=e) = P(Xi|E=e)P(Xj|Xi,E=e)
• Optimal Decisions: decision networks include utility
  information; probabilistic inference required fro
• Value of information: which evidence to seek next?
• Sensitivity analysis: which probability values are most
• Explanation: why do I need a new starter motor?

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        Inference By Enumeration

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            Enumeration Algorithm

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            Evaluation Tree

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   Inference by Variable Elimination

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  Variable Elimination: Basic operations

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            Variable Elimination: Algorithm

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            Irrelevant variables

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            Irrelevant varaibles continued:

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            Complexity of exact inference

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      Inference by stochastic simulation

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            Sampling from empty network

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            Rejection Sampling

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            Analysis of rejection sampling

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            Likelihood weighting

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• Bayesian networks provide a natural representation for
  (causally induced) conditional independence
• Topology + CPTs = compact representation of joint
• Generally easy for domain experts to construct
• Exact inference by variable elimination
     – polytime on polytrees, NP-hard on general graphs
     – space can be exponential as well
     – sampling approaches can help, as they only do approximate
• Take my Graphical Models class if more interested
  (much more theoretical depth)

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