Bayesian Networks

1
Bayesian Networks

• Chapters 14
• Syntax
• Semantics
• Parameterized Distributions

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Definition

• A simple graphical notation for conditional
  independence assertions
• Syntax
• a set of nodes (one per variable)
• a direct acyclic graph (link means directly
  influences)
• a conditional distribution for each node given
  its parents
• P(Xi Parents(Xi))
• In the simplest case, conditional distribution
  is represented as a conditional probability table
  (CPT) giving the distribution over Xi for each
  combination of parent values.

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Example
I am at work. Neighbor, John, calls to say my
alarm is ringing, but neighbor Mary does not
call. Sometimes its set by a minor earthquake.
Is it a burglar? Variables Burglar, Earthquake,
Alarm, JohnCalls, MaryCalls Network topology
reflects causal knowledge A burglar can set
alarm off An earthquake can set alarm off The
alarm can cause Mary call The alarm can cause
John call
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Compactness
A CPT for boolean Xi with k boolean parents has
2k rows for the combinations of parent
values. Each row one number p for Xitrue. If
each variable has no more than k parents, the
complete network Requires O(n 2k) (i.e. grows
linearly with n). For burglary net, 11422
10 numbers.
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Constructing Bayesian networks

• Choose an order of variables X1, , Xn
• 2. For i 1 to n
• add Xi to the network
• select parents from X1, Xi-1 such that
• P(Xi Parents(Xi)) P(Xi X1, Xi-1)
• Thus,
• P( X1, Xn ) ?ni1 P(Xi X1, Xi-1)
• ?ni1 P(Xi Parents(Xi))

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Example
Suppose we choose the ordering M,J,A,B,E. 1.
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Suppose we choose the ordering M,J,A,B,E.2.
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Suppose we choose the ordering M,J,A,B,E. 3.
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Suppose we choose the ordering M,J,A,B,E. 3.
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Suppose we choose the ordering M,J,A,B,E. 3.
Deciding conditional independence is hard in
non-causal directions. Network is less compact 1
2 4 2 4 13
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Example Car diagnosis
Initial evidence car wont start Testable
variables (thin), broken, so fix it variables
(thick) Hidden variables (light gray) help to
reduce parameters
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Compact conditional distributions
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Hybrid (discretecont.) networks
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Cont. child variables
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Discrete variables w/cont. parents
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Why the probit?
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Discrete variable
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Summary