# AP Statistics Curriculum 2007 Bayesian Other

(Difference between revisions)
 Revision as of 05:45, 28 May 2009 (view source)JayZzz (Talk | contribs)← Older edit Revision as of 06:34, 2 June 2009 (view source)JayZzz (Talk | contribs) Newer edit → Line 26: Line 26: (P|x) ~ β(α + x, β + n – x) (P|x) ~ β(α + x, β + n – x) + + + + '''Bayesian Inference for the Poisson Distribution''' + + A discrete random variable x is said to have a Poisson distribution of mean $\lambda$ if it has the density + + + P(x|$\lambda$) = ($\lambda^x 0 + + + Then, given the same sample of n measured values [itex]k_i$ from our likelihood and a prior of Gamma($\alpha$, $\beta$), the posterior distribution becomes + + + $\lambda \sim$ Gamma ($\alpha + \displaystyle\sum_{i=1}^{\infty} k_i$ , $\beta$ + n) + + The posterior mean E[$\lambda$] approaches the maximum likelihood estimate in the limit as $\alpha$ and $\beta$ approach 0.

## Revision as of 06:34, 2 June 2009

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) = ${n \choose x}$ Px (1 − P)nx , (x = 0, 1, …, n)

p(x|P) is proportional to Px(1 − P)nx

If the prior density has the form: p(P) proportional to Pα − 1(P − 1)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 Pα + x − 1(1 − P)β + nx − 1

The posterior distribution of the Binomial is

(P|x) ~ β(α + x, β + n – x)

Bayesian Inference for the Poisson Distribution

A discrete random variable x is said to have a Poisson distribution of mean λ if it has the density

P(x|λ) = (λx < / x!)e − λ

Supose that you have n observations x=(x1, x2, …, xn) from such a distribution so that the likelihood is

L(λ|x) = λTe( − nλ), where T = $\sum{k_i}$

In Bayesian inference, the conjugate prior for the parameter λ of the Poisson distribution is the Gamma distribution.

$\lambda \sim$ Gamma(α , β )

The Poisson parameter λ is distributed accordingly to the parameterized Gamma density g in terms of a shape and inverse scale parameter α and β respectively

g(λ|α , β) = $\displaystyle\frac{\beta^\alpha}{\Gamma(\alpha)}$ λα − 1e − βλ For λ > 0

Then, given the same sample of n measured values ki from our likelihood and a prior of Gamma(α, β), the posterior distribution becomes

$\lambda \sim$ Gamma ($\alpha + \displaystyle\sum_{i=1}^{\infty} k_i$ , β + n)

The posterior mean E[λ] approaches the maximum likelihood estimate in the limit as α and β approach 0.