slides set 6dechter/courses/ics-276/spring... · 2019. 3. 2. · algorithms for reasoning with...
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Algorithms for Reasoning with graphical models
Slides Set 6:
Rina Dechter
slides6 828X 2019
Darwiche chapters 5,
Building Bayesian Networks
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Queries: Different queries may be relevant for different scenarios
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For other tools see class page
http://reasoning.cs.ucla.edu/samiam
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Other type of evidence: We may want to know the probability that the patient has either apositive X-ray or dyspnoea, X =yes or D=yes.
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C= lung cancer
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P(V=yes|E=yes)P(V=yes|E=no) =2
Define a CPT for V that satisfies this constraint
Soft evidence ofPositive x-ray or Dyspnoea(X=yes or D = yes) with odds of 2 to 1.
Modelling: Add E variable andAdd V to model soft evidence.
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MPE is also called MAP
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MPE is also called MAP
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MAP is also called Marginal Map (MMAP)
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Is it correct?
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What about the boundary strata?
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Constructing a Bayesian Network for any Distribution P
Intuition: The causes of X can serve as the parents
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Variables? Arcs? Try it.
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What about?A naive Bayes structurehas the following edges C -> A1, . . . , C -> Am, where C is called
the class variable and A1; : : : ;Am are called the attributes.
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Learn the model from data
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Learning the model
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Try it: Variables and values? Structure? CPTs?
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Read in the book.We will not cover this.
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Try it: Variables? Values? Structure?
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Variables? Values? Structure?
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Try it: Variables, values, structure?
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What queries should we use here?P(Y not equal U) = 0.01
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WER (word error rate), BER (bit error rate)
MAP (MPE) minimizes WER, PM minimize BER…What do you think?
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Notice:Odds: o(x) = P(x)\P(bar(x))K =Bayes factor = o’(x)\o(x) … the posterior odds after observing divided by prior oddsFor Gausian x: evidence on Y=y can be emulated with soft evidence on x with K =f(y|x) \f(y|bar(x)) = the expression above.
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152
Two Loci Inheritance
Recombinant
21A AB B
a ab b
A aB b 3 4
a ab b
A ab b 5 6
A aB b
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153
Bayesian Network for Recombination
S23m
L21fL21m
L23m
X21 S23f
L22fL22m
L23f
X22
X23
S13m
L11fL11m
L13m
X11 S13f
L12fL12m
L13f
X12
X13
y3
y2y1
{m,f}tssP tt
−
−= where
1
1),|( 1323
Locus 1
Locus 2
P(e|Θ) ?
Deterministic relationships
Probabilistic relationships
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154
L11m L11f
X11
L12m L12f
X12
L13m L13f
X13
L14m L14f
X14
L15m L15f
X15
L16m L16f
X16
S13m
S15m
S16mS15m
S15m
S15m
L21m L21f
X21
L22m L22f
X22
L23m L23f
X23
L24m L24f
X24
L25m L25f
X25
L26m L26f
X26
S23m
S25m
S26mS25m
S25m
S25m
L31m L31f
X31
L32m L32f
X32
L33m L33f
X33
L34m L34f
X34
L35m L35f
X35
L36m L36f
X36
S33m
S35m
S36mS35m
S35m
S35m
Linkage analysis: 6 people, 3 markers
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Outline
• Bayesian networks and queries
• Building Bayesian Networks
• Special representations of CPTs
• Causal Independence (e.g., Noisy OR)
• Context Specific Independence
• Determinism
• Mixed Networks