Some adaptive Monte Carlo methods for Bayesian inference

Monte Carlo methods, in particular Markov chain Monte Carlo methods, have become increasingly important as a tool for practical Bayesian inference in recent years. A wide range of algorithms is available, and choosing an algorithm that will work well on a specific problem is challenging. It is therefore important to explore the possibility of developing adaptive strategies that choose and adjust the algorithm to a particular context based on information obtained during sampling as well as information provided with the problem. This paper outlines some of the issues in developing adaptive methods and presents some preliminary results.