# Sampling from conditional distribution in general case

I'm dealing with Gibbs Sampling now. Let's consider the example: I know the distribution of X|Y and the distribution of Y. They are some known - Binomial or Beta or other but particular. Thus I have in analytical view f(X|Y), f(Y) and I can calculate joint distribution f(X,Y). To provide Gibbs Sampling I need to calculate many times X∼X|Y and Y∼Y|X. The question is: which technique can I use to sample Y∼Y|X in general case, for any given f(X|Y), f(Y)?

It is not exactly like that. If it is easy and effective to sample from $X\mid Y=y$ and $Y\mid X=x$, then plain vanilla Gibbs sampling is probably the way to go for sampling from the joint distribution of $X,Y$. There may be cases when it is difficult to sample from some of the full conditionals. In those cases, something like a "Metropolis within Gibbs" algorithm may be a good strategy. Take a look at these notes. A suggestion: I believe that you shouldn't study the subject this way. You must abstract the general concepts working with specific concrete examples. Trying to extend those concrete examples will show you where the difficulties arise. If you're looking for a very good book on the subject, check out the latest edition of Robert and Casella.