Bayesian networks
Chapter 14
Outline
• Syntax
• Semantics
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
Example
• Topology of network encodes conditional independence
assertions:
• Weather is independent of the other variables
• Toothache and Catch are conditionally independent
given Cavity
Example
• I'm at work, neighbor John calls to say my alarm is
ringing, but neighbor Mary doesn't call. Sometimes it's
set off by minor earthquakes. Is there a burglar?
• Variables: Burglary, Earthquake, Alarm, JohnCalls,
MaryCalls
• Network topology reflects "causal" knowledge:
–
–
–
–
A burglar can set the alarm off
An earthquake can set the alarm off
The alarm can cause Mary to call
The alarm can cause John to call
Example contd.
Compactness
• A CPT for Boolean Xi with k Boolean parents has 2k rows for
the combinations of parent values
• Each row requires one number p for Xi = true
(the number for Xi = false is just 1-p)
• If each variable has no more than k parents, the complete
network requires O(n · 2k) numbers
• I.e., grows linearly with n, vs. O(2n) for the full joint distribution
• For burglary net, 1 + 1 + 4 + 2 + 2 = 10 numbers (vs. 25-1 =
31)
Semantics
The full joint distribution is defined as the product of the
local conditional distributions:
n
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)
Constructing Bayesian networks
• 1. Choose an ordering 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)
This choice of parents guarantees:
P (X1, … ,Xn) = πi =1 P (Xi | X1, … , Xi-1)
= πin=1P (Xi | Parents(Xi))
n
(chain rule)
(by construction)
Example
• Suppose we choose the ordering M, J, A, B, E
•
P(J | M) = P(J)?
Example
• Suppose we choose the ordering M, J, A, B, E
•
P(J | M) = P(J) No
P(A | J, M) = P(A | J)? P(A | J, M) = P(A)?
Example
• Suppose we choose the ordering M, J, A, B, E
•
P(J | M) = P(J) No
P(A | J, M) = P(A | J)? P(A | J, M) = P(A)? No
P(B | A, J, M) = P(B | A)?
P(B | A, J, M) = P(B)?
Example
• Suppose we choose the ordering M, J, A, B, E
•
P(J | M) = P(J) No
P(A | J, M) = P(A | J)? P(A | J, M) = P(A)? No
P(B | A, J, M) = P(B | A)? Yes
P(B | A, J, M) = P(B)? No
P(E | B, A ,J, M) = P(E | A)?
P(E | B, A, J, M) = P(E | A, B)?
Example
• Suppose we choose the ordering M, J, A, B, E
•
P(J | M) = P(J) No
P(A | J, M) = P(A | J)? P(A | J, M) = P(A)? No
P(B | A, J, M) = P(B | A)? Yes
P(B | A, J, M) = P(B)? No
P(E | B, A ,J, M) = P(E | A)? No
P(E | B, A, J, M) = P(E | A, B)? Yes
Example contd.
• Deciding conditional independence is hard in noncausal
directions
• (Causal models and conditional independence seem
hardwired for humans!)
• Network is less compact: 1 + 2 + 4 + 2 + 4 = 13 numbers
needed
Inference
• Complexity of inference Bayes Net
– Bayes Net reduces space complexity
– Bayes Net does not reduce time complexity
for general case
Conditional independence and
D-separation
• Two sets of nodes, X and Y, are conditionally independent given an
evidence set of nodes, E if every undirected path from a node in X to
a node in Y is d-seperated by E.
• A set of nodes, E d-separates to sets of nodes, X and Y, if every
undirected path from a node in X to a node in Y is blocked by E
• A path is blocked given E if there is a node Z on the path for which
one of the following holds:
Conditional independence and
D-separation - example
Inference
• Inference by enumeration
P(B | j, m) = P(B, j, m) / P(j, m)
=  P(B, j, m)
=  a
e P(B, e, a, j, m)
=  P(B) e P(e) a P(a | B,e) P(j | a) P(m | a)
Inference for polytrees
Time complexity
• Dynamic programming
– Works well for polytrees (where there is at
most one path between any two nodes)
– Doesn’t work for multiply connected networks
Inference in multiply connected
networks
• Clustering
• Cutset conditioning
• Stochastic methods
– Monte Carlo
– likelihood weighting
– Markov Chain Monte Carlo
• Will be skipped in this course
Clustering
• Merge variables, replace their CPTs by
their combined CPT
Cutset conditioning
• cutting off cycles to obtain polytree.
• Sum over all instantiations of cutset
variables.
Cutset conditioning - example
RC P(W|S,R,C) P(R,C|S)
= RC P(W|S,R) P(R,C|S)
P(W|S) =
P(R,C|S) = P(R,S|C) P(C) / P(S) = P(R|C) P(S|C) P(C) / P(S)
(Notice here that node C d-separates R and S)
P(C = +)
+ P(C = -)
Stochastic Inference
• It’s expensive to work with the full joint
distribution… whether as a table or as a
Bayes Network
• Is approximation good enough?
• Monte Carlo
Monte Carlo
• Use samples to approximate solution
– Simulated annealing used Monte Carlo
theories to justify why random guesses and
sometimes going uphill can lead to optimality
• More samples = better approximation
– How many are needed?
– Where should you take the samples?
Prior sampling
• An ability to model the prior probabilities of
a set of random variables
Approximating true distribution
• With enough samples, perfect modeling is
possible
Rejection sampling
• Compute P(X | e)
– Use PriorSample (SPS) and create N samples
– Denote Ne number of samples consistent with
e (namely E = e)
– Denote Nex number of samples consistent
with E = e) AND X = x
– P(X | e) can be computed from Nex / Ne
Example
– P(Rain | Sprinkler = true)
– Use Bayes Net to generate 100 samples
• Suppose 73 have Sprinkler=false
• Suppose 27 have Sprinkler=true
– 8 have Rain=true
– 19 have Rain=false
– P(Rain | Sprinkler=true) = <8/27; 19/27>
Problems with rejection
sampling
– Standard deviation of the error in probability is
proportional to 1/sqrt(n), where n is the
number of samples consistent with evidence
– As problems become complex, number of
samples consistent with evidence becomes
small and it becomes harder to construct
accurate estimates
Likelihood weighting
• We only want to generate samples that
are consistent with the evidence, e
– We’ll sample the Bayes Net, but we won’t let
every random variable be sampled, some will
be forced to produce a specific output
Example
• P (Rain | Sprinkler=true, WetGrass=true)
Example
• P (Rain | Sprinkler=true, WetGrass=true)
– First, weight vector, w, set to 1.0
Example
• Notice that weight is reduced according to how
likely an evidence variable’s output is given its
parents
– So final probability is a function of what comes from
sampling the free variables while constraining the
evidence variables
Comparing techniques
– In likelihood weighting, attention is paid to
evidence variables before samples are
collected
– In rejection sampling, evidence variables are
considered after the sampling
– Likelihood weighting isn’t as accurate as the
true posterior distribution, P(z | e) because the
sampled variables ignore evidence among z’s
non-ancestors
Likelihood weighting
– Uses all the samples
– As evidence variables increase, it becomes
harder to keep the weighting constant high
and estimate quality drops
Gibbs sampling
• Start with an arbitrary state of the network, with Evidence
variables set to their observed values
• Randomly sample a value for a non evidence variable,
conditioned on its: parents, children and childrens
parents (Markov blanket)
• Each state sampled contributes equally to the estimate
of the query
Some Applications of BN
 Medical diagnosis, e.g., lymph-node diseases
 Troubleshooting of hardware/software systems




Fraud/uncollectible debt detection
Data mining
Analysis of genetic sequences
Data interpretation, computer vision, image
understanding
Identification of buildings in aerial
photographs
Evaluation of insurance risks