 # Solved: (George and Betsy) Find the posterior probabilities below. (a) Probability that Betsy likes

(George and Betsy) Find the posterior probabilities below. (a) Probability that Betsy likes George given Betsy accepts George [Answer format: three decimal places] (b) Probability that Betsy does not like George given Betsy accepts George [Answer format: three decimal places] (c) Probability that Betsy likes George given Betsy rejects George [Answer format: three decimal places] (d) Probability that Betsy does not like George given Betsy rejects George [Answer format: three decimal places] Write your answer(s) as 0.123, 0.456, 0.789, 0.234

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Solved: (George and Betsy) Find the posterior probabilities below. (a) Probability that Betsy likes
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(George and Betsy) George has approached Betsy for a date and been rejected on multiple occasions. He is trying to determine whether Betsy really likes him while playing hard to get or whether she genuinely does not like him. Knowing that probability tree he learned is a useful tool in this case, George first defines the following events: L = Betsy likes George, N = Betsy does not like George, a = Betsy accepts George, r = Betsy rejects George. He is eager to find out P(L|r), the probability that Betsy likes George given her rejection. Using his subjective judgment, he estimates: P(r|L) = the probability of rejection given Betsy likes George = 0.05 George recognizes that this conditional probability must be quite low because Betsy is known to be a very honest and straightforward person. If Betsy actually likes George, he would expect an honest admission from her, rather than repeated rejections. However, women can be funny in showing their affection, so P(r|L) is still greater than 0. It’s very low, but leaves a little room for error in case his perception of Betsy has been mistaken. It is obvious that P(a|L) = 0.95 (Why?) If Betsy does not like George, then it is almost certain she would reject him. Considering Betsy’s honest manner, he assigns: P(r|N) = the probability of rejection given Betsy does not like George = 0.99 It is obvious that P(a|N) = 0.01 (Why?) However, George is popular among women in general and he estimates: P(L) = the probability that Betsy likes George = 0.9 This prior probability must be high since, in determining P(L), the evidence such as the rejections should not count. In other words, P(L) is the probability that Betsy likes George prior to rejections (or the probability that a woman likes George in general). So, he can err on the side of his prominent features such as good looks, size and so on. It is obvious that P(N) = 0.1 (Why?)

P(r|L) = 0.05; P(a|L) = 0.95
P(r|N) = 0.99; P(a|N) = 0.01
P(L) = 0.9; P(N) = 0.10

P(L|r) = ?

 r Prior Conditional Joint* Posterior P(L) 0.900 P(r|L) 0.050 0.045 P(L|r) 0.313 P(N) 0.100 P(r|N) 0.990 0.099 P(N|r) 0.687 Total 1.000 0.144 1.000 a Prior Conditional Joint* Posterior P(L) 0.900 P(a|L) 0.950 0.855 P(L|a) 0.999 P(N) 0.100 P(a|N) 0.010 0.001 P(N|a) 0.001 Total 1.000 0.856 1.000

* Joint Probability = Prior probability x Conditional probability

(a) Probability that Betsy likes George given Betsy accepts George [P(L|a) = 0.999]
(b) Probability that Betsy does not like George given Betsy accepts George [P(N|a) = 0.001]
(c) Probability that Betsy likes George given Betsy rejects George [P(L|r) = 0.313]
(d) Probability that Betsy does not like George given Betsy rejects George [P(N|r) = 0.687]