# AP Statistics Curriculum 2007 Bayesian Other

### From Socr

Bayesian Inference for the Binomial Distribution

The parameters of interest in this section is the probability P of success in a number of trials which can result in either success or failure with the trials being independent of one another and having the same probability of success. Suppose that there are n trials such that you have an observation of x successes from a binomial distribution of index n and parameter P

x ~ B(n,P)

subsequently, we can show that
p(x|P) = *P*^{x} (1 − *P*)^{n − x} , (x = 0, 1, …, n)

p(x|P) is proportional to *P*^{x}(1 − *P*)^{n − x}

If the prior density has the form:
p(P) proportional to **Failed to parse (lexing error): P^{α - 1}**
(1 – P)β – 1 , (P between 0 and 1)

then it follows the beta distribution P ~ β(α,β)

From this we can appropriate the posterior which evidently has the form

p(P|x) is proportional to **Failed to parse (lexing error): P^{α + x – 1}**
(1 – P)^{β + n – x – 1}

The posterior distribution of the Binomial is

(P|x) ~ β(α + x, β + n – x)