Past exam of the statistics course of the University of Oxford / 2021 / 3 / a / iii

The prior mean $1/(\beta +1)$ decreases as $\beta$ increases. More strongly, its cumulative distribution function and upper-tail probability areFor every fixed $t$, that upper-tail probability decreases with $\beta$. The statistician increasingly favors smaller values of $\theta$; as $\beta \to \infty$, the prior distribution concentrates near zero. For $\beta >1$, its variance $\beta /[(\beta +1{)}^2(\beta +2)]$ also decreases. This is concentration of a beta prior near zero.

With the data fixed, the derivatives areThus the prior weight increases and the posterior mean decreases. Their limits are $\lambda \to 1$ and ${\tilde{\theta }}_1\to 0$. Both the changing prior mean and the increasing weight contribute to the change in the posterior mean.