David Hastie

Title: Towards automatic reversible jump Markov chain Monte Carlo

Abstract:

Since its introduction by Green (1995), reversible jump MCMC has been recognised as a powerful tool for making posterior inference about a wide range of statistical problems. Although the method has been applied to many problems within a variety of disciplines, its popularity has been tempered by the common perception that reversible jump samplers can be difficult to implement.

This talk presents steps towards the design of an automatic reversible jump sampler, with the aim of taking the method outside the domain of the MCMC expert. Generalising the first steps taken by Green (2003), the generic AutoMix sampler is introduced. Requiring minimal user input, the AutoMix sampler uses adaptive techniques to perform self-tuning and calibration, allowing the consideration of many trans-dimensional statistical problems.

The main features of the sampler are detailed, demonstrating the sampler's broad applicability. We present examples of the AutoMix sampler in practice, indicating performance that is at least as good as problem-specific samplers, designed and tuned by hand. Finally, we discuss potential areas for future research.