If I know how to simulate the distribution $\pi(\mathbf{x})$, then is there a way to directly generate samples of $(\pi(\mathbf{x}))^\beta$ for some $\beta > 0$ ?(assume that it can be normalised to a density, i.e. $\int (\pi(\mathbf{x}))^\beta d\mathbf{x} < \infty$ ) What I'm interested in is a example like this if $\pi = 0.5 N(\mu_1, \Sigma_1) + 0.5 N(\mu_2, \Sigma_2)$, and I know how to sample $\pi$ from Generating random variables from a mixture of Normal distributions, but how can I sample from $\pi^\beta$ (without using MCMC algorithms).
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But I'm not sure what you mean by this question.
– Cliff AB May 18 '15 at 16:31