# Find posterior distribution

Let $X_{1},..,X_{n}$ be a sample from a poisson$({\lambda})$ distribution. Let the prior be ${\pi}({\lambda})=1/{\sqrt{\lambda}}$. Find the posterior distribution.

My work: We have $f(x|{\lambda})=\frac{{\lambda}^{x_{1}+..+x_{n}-0.5}e^{-{\lambda}n}}{x_{1}!....x_{n}!}$. To find the marginal density I believe I need to sum this expression through ${\lambda}=0,1,2,......$. How to do this?

Thanks

• Can you show us what you have tried so far? Without some input from your side this question is unlikely to be fully answered for you by somebody else.
– Andy
Commented May 3, 2014 at 14:21
• @Andy I have shown my work by finding (hopefully correct) $f(x|{\lambda})$ Commented May 3, 2014 at 14:26
• What you found so far is the likelihood for the Poisson distribution (where does this -0.5 in the exponent of $\lambda$ come from? It should just be $\lambda ^{\sum^{n}_{i=1} x_i}$). As hint for the final step: use Bayes' rule to find the posterior
– Andy
Commented May 3, 2014 at 14:35
• @Andy I've multipled by the prior, so actually I should have written $f(x,{\theta})$. Now I need to find the marginal density to use bayes rule. Can you please help me a bit more? Commented May 3, 2014 at 14:52
• Ah now I get it, sorry I guess I was a bit blind earlier. Just forget my previous comments.
– Andy
Commented May 3, 2014 at 15:01

$p(\lambda|X)=\frac{p(X|\lambda)\pi(\lambda)}{p(X)}=\frac{L_\lambda(X)\pi(\lambda)}{\int_0^\infty p(X,\lambda)d\lambda}$

$L_\lambda(X)=\frac{e^{-n\lambda }\lambda^{\sum x_i}}{\Pi(x_i!)}$

$\int_0^\infty p(X,\lambda)d\lambda=constant$

$p(\lambda|X)\propto \pi(\lambda)L_\lambda(X)=\frac{e^{-n\lambda }\lambda^{\sum x_i-0.5}}{\Pi(x_i!)}$

$\int_0^\infty p(X,\lambda)d\lambda=\int_0^\infty p(\lambda)(X|\lambda)d\lambda$, since we believe $Cov(\lambda,x_i)=0$

$\int_0^\infty p(\lambda)(X|\lambda)d\lambda=\int_0^\infty\frac{e^{-n\lambda }\lambda^{\sum x_i-0.5}}{\Pi(x_i!)}d\lambda=\int_0^\infty\frac{1}{n^{\sum x_i+0.5}\Pi(x_i!)}\times e^{-n\lambda }(n\lambda)^{\sum x_i-0.5}d(n\lambda)$

The result of the integral is a Gamma function

$\int_0^\infty\frac{1}{n^{\sum x_i+0.5}\Pi(x_i!)}\times e^{-n\lambda }(n\lambda)^{\sum x_i-0.5}d(n\lambda)=\frac{\Gamma(\sum x_i+0.5)}{n^{\sum x_i+0.5}\Pi(x_i!)}$

$p(\lambda|X)=\frac{e^{-n\lambda }\lambda^{\sum x_i-0.5}}{\Pi(x_i!)}\div\frac{\Gamma(\sum x_i+0.5)}{n^{\sum x_i+0.5}\Pi(x_i!)}=\frac{exp\{-n\lambda\}\lambda^{\sum x_i-0.5}n^{\sum x_i+0.5}}{\Gamma(\sum x_i+0.5)}$

$f(\lambda|X)$ is a $\gamma$ distribution as $f(\lambda|X;\alpha,\beta)$, where $alpha=\sum x_i+0.5$ and $\beta=n$

$\Gamma(\sum x_i+0.5)=\frac{(2\sum x_i)!\sqrt\pi}{(\sum x_i)!4^{\sum x_i}}$

$p(\lambda|X)=\frac{exp\{-n\lambda\}\lambda^{\sum x_i-0.5}n^{\sum x_i+0.5}(\sum x_i)!4^{\sum x_i}}{(2\sum x_i)!\sqrt\pi}$

And from the Bayesian Analysis, we have much confidence to believe that the probability of $\lambda$ of a Poisson distribution to be $\gamma$ distribution, almost regardless of the prior $\pi(\lambda)$.

That's much the reason, I believe, that we usually assume $\pi(\lambda)=\gamma(\lambda;\alpha,\beta)$, a $\gamma$ function for the prior of the parameter $\lambda$ of the Possion distrubtion Pois($\lambda$).

And we usually use flat prior for $\alpha,\beta$, namely $\alpha\sim U,\beta\sim U$, to acknowledge that we have no information of $\alpha$ and $\beta$.

In this case we have a hierarchical prior.

A gamma prior $\gamma(\lambda;\alpha,\beta)$ for $\lambda$ of the Poisson distribution Pois($\lambda$).

Flat hyperpriors $\alpha\sim U, \beta\sim U$ for $\alpha$ and $\beta$ of the $\gamma(\lambda;\alpha,\beta)$ prior.

The above discussion, I think, is pretty common in standard text books regarding priors.