In the context of a lot of MCMC methods for sampling you seem to need a sampler candidate distribution Q and an un-normalized version of your target distribution P. (Where you are trying to draw samples from P) I'm wondering how this P can arise without having access to P in the context of Bayesian Inference?
[link] [5 comments]