I have a density that can be written as follows,
$$ p(\mathbf{z}) = \frac{1}{H_f}\prod_iq_i(\mathbf{z}) $$
I want to sample from the density $p(\mathbf{z})$ using samples from each of $q_i(\mathbf{z})$, and there are a bunch of algorithms that let me do this, according to the answer to this question here - Previous similar question
In those solutions, we are required to draw samples from $q_i(\mathbf{z})$. In the problem I am faced with, my $q_i(\mathbf{z})$ has the following form
$$ q_i(\mathbf{z}) = [h_i(\mathbf{z})]^{\alpha_i} $$ where $\alpha_i \in \mathbb{R}^{+}$ so the overall density can be written as $$ p(\mathbf{z}) = \frac{1}{H_f}\prod_i[h_i(\mathbf{z})]^{\alpha_i} $$ I know how to sample from $h_i(\mathbf{z})$ without MCMC techniques - $h_i(\mathbf{z})$ is an exponential family distribution that I have chosen such that I can sample from it easily
My question is as follows
- How do I sample from the unnormalised density $[h_i(\mathbf{z})]^{\alpha_i}$, given I can sample from $h_i(\mathbf{z})$
2.Can I still use the kind of techniques in the above reference?
3.Are there more modern approaches that aren't direct followups from the papers in that question?