MCMC when you do not want to evaluate the target distribution

Oregon Convention Center 

In sampling tasks, it is common for target distributions to be known up to a normalizing constant. However, in numerous situations, evaluating even the unnormalized distribution proves to be costly or infeasible. This issue arises in scenarios such as sampling from the Bayesian posterior for large datasets and the 'doubly intractable' distributions. Our work introduces a unified framework that includes various MCMC algorithms, including several minibatch MCMC algorithms and the exchange algorithm. This framework not only simplifies the theoretical analysis of existing algorithms but also paves the way for the development of new, more efficient algorithms.