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Topic: interesting probability problem
Replies: 9   Last Post: Oct 17, 2007 3:20 AM

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Posts: 130
Registered: 5/22/05
interesting probability problem
Posted: Oct 16, 2007 6:25 AM
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Bertsekas has the following exercise in his probability book:

Consider a statement whose truth is unknown. If we see many examples
that are compatible with it, we are tempted to view the statement as
more probable. Such reasoning is often referred to as inductive
inference (in a philosophical, rather than mathematical sense). Consider
now the statement that ?all cows are white.? An equivalent statement is
that ?everything that is not white is not a cow.? We then observe
several black crows. Our observations are clearly compatible with the
statement, but do they make the hypothesis ?all cows are white? more likely?

To analyze such a situation, we consider a probabilistic model. Let us
assume that there are two possible states of the world, which we model
as complementary events:

A : all cows are white,
C(A) : 50% of all cows are white. [C(A) is the complement event of A].

Let p be the prior probability P(A) that all cows are white. We make an
observation of a cow or a crow, with probability q and 1?q,
respectively, independently of whether event A occurs or not. Assume
that 0 < p < 1, 0 < q < 1, and that all crows are black.

(a) Given the event B = {a black crow was observed}, what is P(A|B)?
(b) Given the event C = {a white cow was observed}, what is P(A|C)?

Solutions to a) is p, and b) is 2p/(1-p). From this he draws the
conclusion that a) does not affect the hypothesis A, while b)
strengthens it. Is this reasoning correct? I mean event B should have
the same effect as event C, as B supports A, since A is equivalent to
"everything that is not white is not a cow". What is wrong in my reasoning?

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