Sequentially interacting Markov Chain Monte Carlo
Seminar Room 1, Newton Institute
We introduce a methodology to sample from a sequence of probability distributions and estimate their unknown normalizing constants. This problem is traditionally addressed using Sequential Monte Carlo (SMC) methods which rely on importance sampling/resampling ideas. We design here an alternative iterative algorithm. This algorithm is based on a sequence of interacting Markov chain Monte Carlo (MCMC) algorithms. We establish the convergence of this non-Markovian scheme and demonstrate this methodology on various examples arising in Bayesian inference.
(This is a joint work with Anthony E. Brockwell, Department of Statistics, Carnegie Mellon)