Past exam of the statistics course of the University of Oxford / 2023 / 3 / b

Writing $s={\sum }_i{x}_i$, the Poisson distribution likelihood is proportional to ${\theta }^s{e}^{-n\theta }$. Multiplying by the gamma distribution prior givesThis is Poisson-gamma conjugacy, with the rate convention made explicit:Its normalized density is $(\lambda +n{)}^{r+s}{\theta }^{r+s-1}{e}^{-(\lambda +n)\theta }/\Gamma (r+s)$ for $\theta >0$.

The posterior mean is a convex combination of the prior mean and the usual Poisson maximum-likelihood estimate:Both weights are positive and sum to one, so the mean lies between $r/\lambda$ and $\bar{x}$, strictly between them unless they coincide. If all counts are zero, $\bar{x}=0$ is the boundary estimate in the usual nonnegative Poisson parameter space; with a strictly positive parameter restriction it represents the likelihood supremum at that boundary. The displayed posterior-mean relation is valid in either interpretation.