==> probability/apriori.s <==
This question cannot be answered with the information given.
In general, the following formula gives the conditional probability
that all the balls are white given you have sampled one hundred balls
and they are all white:
P(100 white | 100 white samples) =
P(100 white samples | 100 white) * P(100 white)
-----------------------------------------------------------
sum(i=0 to 100) P(100 white samples | i white) * P(i white)
The probabilities P(i white) are needed to compute this formula. This
does not seem helpful, since one of these (P(100 white)) is just what we
are trying to compute. However, the following argument can be made:
Before the experiment, all possible numbers of white balls from zero to
one hundred are equally likely, so P(i white) = 1/101. Therefore, the
odds that all 100 balls are white given 100 white samples is:
P(100 white | 100 white samples) =
1 / ( sum(i=0 to 100) (i/100)^100 ) =
63.6%
This argument is fallacious, however, since we cannot assume that the urn
was prepared so that all possible numbers of white balls from zero to one
hundred are equally likely. In general, we need to know the P(i white)
in order to calculate the P(100 white | 100 white samples). Without this
information, we cannot determine the answer.
This leads to a general "problem": our judgments about the relative
likelihood of things is based on past experience. Each experience allows
us to adjust our likelihood judgment, based on prior probabilities. This
is called Bayesian inference. However, if the prior probabilities are not
known, then neither are the derived probabilities. But how are the prior
probabilities determined? For example, if we are brains in the vat of a
diabolical scientist, all of our prior experiences are illusions, and
therefore all of our prior probabilities are wrong.
All of our probability judgments indeed depend upon the assumption that
we are not brains in a vat. If this assumption is wrong, all bets are
off.