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Transcript of QUIZ!! T/F: Probability Tables PT(X) do not always sum to one. FALSE T/F: Conditional Probability...
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QUIZ!!
T/F: Probability Tables PT(X) do not always sum to one. FALSE T/F: Conditional Probability Tables CPT(X|Y=y) always sum to one. TRUE T/F: Conditional Probability Tables CPT(X|Y) always sum to one. FALSE T/F: Marginal distr. can be computed from joint distributions. TRUE T/F: P(X|Y)P(Y)=P(X,Y)=P(Y|X)P(X). TRUE T/F: A probabilistic model is a joint distribution over a set of r.v.’s. TRUE T/F: Probabilistic inference = compute conditional probs. from joint. TRUE
What is the power of Bayes Rule? Name the three steps of Inference by Enumeration.
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Inference by Enumeration General case:
Evidence variables: Query* variable: Hidden variables:
We want:
First, select the entries consistent with the evidence Second, sum out H to get joint of Query and evidence:
Third, normalize the remaining entries to conditionalize
Obvious problems: Worst-case time complexity O(dn) Space complexity O(dn) to store the joint distribution
All variables
* Works fine with multiple query variables, too
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CSE 511a: Artificial IntelligenceSpring 2012
Lecture 14: Bayes’Nets
/ Graphical Models
10/25/2010
Kilian Q. Weinberger
Many slides adapted from Dan Klein – UC Berkeley
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Last Lecture ...
Probabilistic Models Inference by Enumeration Inference with Bayes Rule
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Works well for small problems, but what if we have many random variables?
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This Lecture: Bayes Nets
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Probabilistic Models A probabilistic model is a joint distribution
over a set of random variables
Probabilistic models: (Random) variables with domains
Assignments are called outcomes Joint distributions: say whether
assignments (outcomes) are likely Normalized: sum to 1.0 Ideally: only certain variables directly
interact
T W P
hot sun 0.4
hot rain 0.1
cold sun 0.2
cold rain 0.3
Distribution over T,W
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Probabilistic Models
Models describe how (a portion of) the world works
Models are always simplifications May not account for every variable May not account for all interactions between variables “All models are wrong; but some are useful.”
– George E. P. Box
What do we do with probabilistic models? We (or our agents) need to reason about unknown variables,
given evidence Example: explanation (diagnostic reasoning) Example: prediction (causal reasoning) Example: value of information
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Probabilistic Models
A probabilistic model is a joint distribution over a set of variables
Given a joint distribution, we can reason about unobserved variables given observations (evidence)
General form of a query:
This kind of posterior distribution is also called the belief function of an agent which uses this model
Stuff you care about
Stuff you already know
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Independence
Two variables are independent in a joint distribution if and only if:
Says the joint distribution factors into a product of two simple ones Usually variables aren’t independent!
Can use independence as a modeling assumption Independence can be a simplifying assumption Empirical joint distributions: at best “close” to independent What could we assume for {Weather, Traffic, Cavity}?
Independence is like something from CSPs: what?10
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Example: Independence
N fair, independent coin flips:
h 0.5
t 0.5
h 0.5
t 0.5
h 0.5
t 0.5
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Example: Independence?
T W P
warm sun 0.4
warm rain 0.1
cold sun 0.2
cold rain 0.3
T W P
warm sun 0.3
warm rain 0.2
cold sun 0.3
cold rain 0.2
T P
warm 0.5
cold 0.5
W P
sun 0.6
rain 0.4
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Conditional Independence P(Toothache, Cavity, Catch)
If I have a cavity, the probability that the probe catches in it doesn't depend on whether I have a toothache: P(+catch | +toothache, +cavity) = P(+catch | +cavity)
The same independence holds if I don’t have a cavity: P(+catch | +toothache, cavity) = P(+catch| cavity)
Catch is conditionally independent of Toothache given Cavity: P(Catch | Toothache, Cavity) = P(Catch | Cavity)
Equivalent statements: P(Toothache | Catch , Cavity) = P(Toothache | Cavity) P(Toothache, Catch | Cavity) = P(Toothache | Cavity) P(Catch | Cavity) One can be derived from the other easily
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Conditional Independence
Unconditional (absolute) independence very rare
Conditional independence is our most basic and robust form of knowledge about uncertain environments:
What about this domain: Traffic Umbrella Raining
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The Chain Rule
Trivial decomposition:
With assumption of conditional independence:
Bayes’ nets / graphical models help us express conditional independence assumptions 15
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Bayes’ Nets: Big Picture
Two problems with using full joint distribution tables as our probabilistic models: Unless there are only a few variables, the joint is WAY too big to
represent explicitly Hard to learn (estimate) anything empirically about more than a
few variables at a time
Bayes’ nets: a technique for describing complex joint distributions (models) using simple, local distributions (conditional probabilities) More properly called graphical models We describe how variables locally interact Local interactions chain together to give global, indirect
interactions For about 10 min, we’ll be vague about how these interactions are
specified17
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Graphical Model Notation
Nodes: variables (with domains) Can be assigned (observed) or
unassigned (unobserved)
Arcs: interactions Similar to CSP constraints Indicate “direct influence” between
variables Formally: encode conditional
independence (more later)
For now: imagine that arrows mean direct causation (in general, they don’t!)
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Example Bayes’ Net: Car
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Example: Coin Flips
X1 X2 Xn
N independent coin flips
No interactions between variables: absolute independence
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Example: Traffic
Variables: R: It rains T: There is traffic
Model 1: independence
Model 2: rain causes traffic
Why is an agent using model 2 better?
R
T
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Example: Traffic II
Let’s build a causal graphical model
Variables T: Traffic R: It rains L: Low pressure D: Roof drips B: Ballgame C: Cavity
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Example: Alarm Network
Variables B: Burglary A: Alarm goes off M: Mary calls J: John calls E: Earthquake!
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Does smoking cause cancer?
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Smoking
Cancer
In 1950s, suspicion:
Correlation discovered by Ernst Wynder, at WashU 1948.
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Does smoking cause cancer?
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UnknownGene
CancerSmoking
Explanation of the Tobacco Research Council:
Link between smoking and cancer finally established in 1998. (22 Million deaths due to tobacco in those 50 years.)
P(cancer | smoking, gene)=P(cancer | gene)
Correlation discovered by Ernst Wynder, at WashU 1948.
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Global Warming
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Green HouseGases
ClimateChange
HumanActivity
Model: 1.Explains data2.Makes verifiable predictions
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Global Warming
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UnknownCause X
ClimateChange
Green HouseGases
HumanActivity
Model: 1.Undefined (mystery) variables2.Does not explain data3.Makes no predictions
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Bayes’ Net Semantics
Let’s formalize the semantics of a Bayes’ net
A set of nodes, one per variable (A’s and X’s)
A directed, acyclic graph A conditional distribution for each node
A collection of distributions over X, one for each combination of parents’ values
CPT: conditional probability table Description of a noisy “causal” process
A1
X
An
A Bayes net = Topology (graph) + Local Conditional Probabilities29
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Probabilities in BNs
Bayes’ nets implicitly encode joint distributions As a product of local conditional distributions To see what probability a BN gives to a full assignment, multiply
all the relevant conditionals together:
Example:
This lets us reconstruct any entry of the full joint Not every BN can represent every joint distribution
The topology enforces certain conditional independencies30
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Example: Coin Flips
h 0.5
t 0.5
h 0.5
t 0.5
h 0.5
t 0.5
X1 X2 Xn
Only distributions whose variables are absolutely independent can be represented by a Bayes’ net with no arcs. 31
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Example: Traffic
R
T
+r 1/4
r 3/4
+r +t 3/4
t 1/4
r +t 1/2
t 1/2
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Example: Alarm Network
Burglary Earthqk
Alarm
John calls
Mary calls
B P(B)
+b 0.001
b 0.999
E P(E)
+e 0.002
e 0.998
B E A P(A|B,E)
+b +e +a 0.95
+b +e a 0.05
+b e +a 0.94
+b e a 0.06
b +e +a 0.29
b +e a 0.71
b e +a 0.001
b e a 0.999
A J P(J|A)
+a +j 0.9
+a j 0.1
a +j 0.05
a j 0.95
A M P(M|A)
+a +m 0.7
+a m 0.3
a +m 0.01
a m 0.99
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Bayes’ Nets
So far: how a Bayes’ net encodes a joint distribution
Next: how to answer queries about that distribution Key idea: conditional independence Last class: assembled BNs using an intuitive notion of
conditional independence as causality Today: formalize these ideas Main goal: answer queries about conditional independence and
influence
After that: how to answer numerical queries (inference)
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