So suppose PDF $f_{X|\theta}(x_1,...,x_n;\theta_1,...,\theta_m)$ is from the exponential family. Is there any theory or general guidelines for sampling parameters from this PDF?
This question is not about sampling methods like Gibbs, Metropolis-Hastings, Importance, Slice, etc. Rather I wonder whether I could exploit the fact that $X$ is from the exponential family such as to write an efficient sampler for $\theta$?
$$ f_{\theta|X}(\theta|x) = \frac{f_{X|\theta}(x|\theta)f(\theta)}{f(x)} $$
Lets just suppose that $f(\theta)$ is uniform. In that case this is equivalent as sampling $\theta$ from the likelihood $L_{\theta|X}(\theta;x)$ in the frequentist framework.
So this is a very general and theoretical question. Ideas and pointers are appreciated.