Next: Bayes rule
Up: Events and probabilities
Previous: Probabilities:
Conditional probabilities give
us the formal tools which allow us to talk about dependencies between
events. We could model the patterns of language use in the
Conan-Doyle story using word-confetti, but that would leave out the
evident fact that ``Holmes'' follows ``Sherlock''
just as night follows day. The formal statement of this fact is that
the conditional probability of the nth word being ``Holmes'' if
the n-1th
is ``Sherlock'' appears to be 1 for Conan-Doyle stories. The notation
for this is
P(Wn = holmes | Wn-1 = sherlock) = 1
We also have notation for the joint event of the n-1th
word being ``Sherlock'' and the nth ``Holmes''. This is:
P(Wn = holmes,Wn-1 = sherlock)
Because
we are absolutely certain of the identity of the next word
when we have seen the ``Sherlock'', it follows that:
P(Wn = holmes,Wn-1 = sherlock) =

P(Wn-1 = sherlock)
In general, for any pair of words, we will
have:

which is usually written more compactly:

While it is true that P(holmesn| sherlockn-1) = 1, it is
definitely not true that P(sherlockn-1| holmesn) = 1, because
the word ``holmes'' occurs frequently in contexts where it is preceded
by something other than ``sherlock''. If someone tells us that the
354th word of the story is ``holmes'' (I haven't checked), then we
cannot be certain that the 353rd is ``sherlock''. There is a better
than even chance, but we cannot be sure.
It remains the case that
.
Next: Bayes rule
Up: Events and probabilities
Previous: Probabilities:
Chris Brew
8/7/1998