Past exam of the mathematics course of the University of Cambridge / 2016 / ib / Paper 3 / 20H / c / Solution

The specified gamma distribution uses shape $k$ and rate $\lambda$. Multiplying its density by the Poisson likelihood gives a posterior density proportional to ${\theta }^{k+S-1}{e}^{-(\lambda +n)\theta }$. Thus the conjugate prior calculation givesFor squared-error loss, the posterior expected loss isIt is minimized at the posterior mean, so the Bayes estimator isFor a fresh conditionally independent Poisson observation, the posterior predictive probability of zero is the posterior expectation of $e^{-\theta }$. Evaluating the gamma integral givesThe samples are independent conditional on $\theta$; integrating out the uncertain common parameter produces the predictive probability above.