 Solution

for an estimator $\delta (X)$ and loss $L$, its risk function is $R(p,\delta )={\mathbb{E}}_{p}L(\delta (X),p)$, where expectation is over repeated data at fixed $p$. Its Bayes risk under prior $\pi$ is $r(\pi ,\delta )=\int R(p,\delta )\pi (dp)$, equivalently the joint expectation under the prior and sampling model.