Conditional Probabilities and Bayes’ Theorem
With the Boltzmann distribution (2.26) we have already met a distribution where
certain given parameters need to be included explicitly, for instance, the temperature
T and the volume V . The number of particles N may also be counted among these
given parameters. In probability theory one writes A j B for an event A under the
condition that B is given. So the probability P.A/ is then more precisely denoted
by P.A j B/, i.e., the probability of A when B is given. P.A j B/ is called the
conditional probability.
This notion extends to the probability densities. The Boltzmann distribution can
therefore be written as
%.p; q j T; V;N/; (