# Document 6590118

## Transcription

Document 6590118
```562
Chapter 14.
FL
F2
P
Mt
Probabilistic Reasoning
®
(I)
Figure 14.22
Three possible networks for the telescope problem.
14.12 Two astronomers in different parts of the world make measurements Mi. and M2 of
the number of stars N in some small region of the sky, using their telescopes. Normally, there
is a small possibility e of error by up to one star in each direction. Each telescope can also
(with a much smaller probability f) be badly out of focus (events Ft and F2 ), in which case
the scientist will undercount by three or more stars (or if N is less than 3, fail to detect any
stars at all). Consider the three networks shown in Figure 14.22.
a. Which of these Bayesian networks are correct (but not necessarily efficient) representations of the preceding information?
b. Which is the best network? Explain_
c. Write out a conditional distribution for P(11.1i N), for the case where N E {1, 2, 3} and
e (0, 1, 2, 3, 4}. Each entry in the conditional distribution should be expressed as a
function of the parameters e and/or f.
d. Suppose M1 =1 and M2 = 3. What are the possible numbers of stars if you assume no
prior constraint on the values of N?
e. What is the frost likely number of stars, given these observations? Explain how to
compute this, or if it is not possible to compute, explain what additional information is
needed and how it would affect the result.
14.13 Consider the network shown in Figure 14.22(ii), and assume that the two telescopes
work identically. N e {1,2, 3} and MI, M2 E {0, 1, 2, 3, 4}, with the symbolic CPTs as described in Exercise 14.12. Using the enumeration algorithm (Figure 14.9 on page 525), calculate the probability distribution P(N I Mi. = 2, M2 = 2).
14.14 Consider the Bayes net shown in Figure 14.23.
a. Which of the following are asserted by the network structure?
(i) PCB .1,111) = P(B)P(I)P;11.1).
(ill P(JI 0 ) — P(J 1 0 , 1)G, B, ,
( E) P(M G, B I) =
Exercises
563
r(bo I
Figure 14.23
A simple Bayes net with Boolean variables B = BrokeElectioriLuP,
I = Indicted M = PcliticallyMotivatedProseentar, G = FatindGuilty, J = Jailed.
,
Calculate the value of P(b,
g, j).
e. Calculate the probability that someone goes to jail given that they broke the law, have
been indicted, and face a politically motivated prosecutor.
d. A cunlexl-specific independence (see page 542) allows a variable to be independent
of some of its parents given certain values of others. In addition to the usual conditional
independences given by the graph structure, what context-specific independences exist
in the Bayes net in Figure 14.23?
e. Suppose we want to add the variable P = PresidentialPardanto the network; draw the
b.
14_15
Consider the variable elimination algorithm in Figure 14.11 (page 528).
a. Section 14.4 applies variable elimination to the query
P(Burgittry
JolinCalls = true, Mary Calls = true)
Perform the calculations indicated and check that the answer is correct.
b. Count the number of arithmetic operations performed, and compare it with the number
performed by the enumeration algorithm.
e. Suppose a network has the form of a chain: a sequence of Boolean variables X i , , X„
where Parents(Xi ) = {X i _ 1 } for = 2, ... , n. What is the complexity of computing
P(Xi I = true) using enumeration? Using variable elimination?
d. Prove that the complexity of running variable elimination on a polytree network is linear
in the size of the tree for any variable ordering consistent with the network structure.
14.16
Investigate the complexity of exact inference in general Bayesian networks:
3-SAT problem can be reduced to exact inference in a Bayesian network
constructed to represent the particular problem and hence that exact inference is NP-
a. Prove that any
```

### Chris Geary - Harness Racing NSW ### January 11 - Black Rider Press ### HOW TO STICK YOUR COLLECT STARS TOMICA STARS HERE ### Registration form copy ### “Baby Stars in the Universe” ### Page 6b Entertainment Page - The Fayetteville Press Newspaper ### Palace Cinemas ### Video GameMercials   ### ARPA/Rome Proposal - Advances in Computer Science Research ### Bayesian Networks ### DON BOSCO INSTITUTE OF TECHNOLOGY ### NESS2015sc ### Math 116 - Section 1.1 ### slides ### Bayesian Sample Size Determination in Two ### Inference on Optimal Treatment Assignments ### You May Believe You Are a Bayesian But You Are ### Statistics 102 Unit 2 Probability Suggested Reading Open   