Past exam of the mathematics course of the University of Cambridge / 2014 / iii / Paper 35 / 1 / f / Solution

There is a notation problem here. The posterior predictive probability $p_0$ calculated above is already a fixed number conditional on $a,b,n,T$. Literally,No simulation is needed for that interpretation. The known exposure $T$ is also necessary, despite its omission from this part's list of inputs.

The natural uncertain quantity is instead $q(\lambda )=\mathbb{P}(M=0\mid \lambda )=e^{-\lambda }$. For its posterior probability of being below one half, draw the rate from its gamma distribution Bayesian posterior and average an indicator. Rough BUGS code isThe monitored average of belowHalf estimates $\mathbb{P}(\lambda >\log 2\mid \mathcal{D})$, equivalently $1-F_{\mathrm{Gamma}(A,B)}(\log 2)$. The equality boundary has zero probability. This code samples the already updated Bayesian posterior; adding the count likelihood function again would count the data twice.