the question is as follows.
Assume the shape $r$ is a known constant. For $x \sim$ Gamma(shape = $r$, rate = $v$), the p.d.f is:
$$p(x|r,v) = x^{r-1}e^{-vx}v^r/\Gamma(r)$$
a) Show that the $\theta \sim$ Gamma$(r_0, v_0)$ prior for $\theta$ is the conjugate prior for a Gamma($r,\theta$) likelihood. Do this by finding the exact posterior distribution of $\theta|y,$ where $y=(y_1, y_2,…y_n)$ and each $y_i \sim$ Gamma $(r, \theta)$.
b) Generalize this conjugacy result to a more general multiplicative relationship. Assume $\theta \sim$ Gamma$(r_0, v_0)$ prior distribution. Now, the rate parameter of the likelihood is $c\theta$, where $c$ is a known constant. That is, each $y_i \sim$ Gamma$(r, c\theta)$. Find the exact form of the posterior distribution of $\theta|y$ which will now also involve $c$.
c) What choices of $r_0$ and $v_0$ will minimize the effect of the prior distribution on the posterior distribution?
I'm unsure about how to solve this question. For part (a), how do I find the exact posterior distribution of $\theta|y$, and proceed further? I thought the posterior distribution was likelihood times prior, but I'm not sure how that applies here, and how to show that the asked for prior is a conjugate prior for the Gamma likelihood.
Similarly for part (b) and (c), I'm a bit confused as I'm new to Bayesian and proof-based Math. Would appreciate any detailed help – thank you in advance!
Best Answer
As you mention, the proof only need consider the product "prior x likelihood"
which shows conjugacy.
This is a straightforward generalisation since
As for the last question,
it is an ill-posed question since the "effect" to be assessed is not formally defined and clearly spelled-out. Is the effect computed via the Kullback-Leibler distance [as in Bernardo's & Berger's reference priors]? or via a maximum risk (if it exists)?