Possibly pithy insights into computer performance analysis and capacity planning based on the Guerrilla series of books and training classes provided by Performance Dynamics Company.
Sunday, September 15, 2013
Laplace the Bayesianista and the Mass of Saturn
I'm reviewing Bayes' theorem and related topics for the upcoming GDAT class. In its simplest form, Bayes' theorem is a statement about conditional probabilities. The probability of A, given that B has occurred, is expressed as:
\begin{equation}
\Pr(A|B) = \dfrac{\Pr(B|A)\times\Pr(A)}{\Pr(B)} \label{eqn:bayes}
\end{equation}
In Bayesian language, $\Pr(A|B)$ is called the posterior probability, $\Pr(A)$ the prior probability, and $\Pr(B|A)$ the likelihood (essentially a normalization factor).
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