Generalized darting Monte Carlo

One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge of the location of these modes can be incorporated into the MCMC sampler by introducing mode-hopping moves that satisfy detailed balance. The proposed sampling algorithm explores local mode structure through local MCMC moves (e.g. diffusion or Hybrid Monte Carlo) but in addition also represents the relative strengths of the different modes correctly using a set of global moves. This ‘mode-hopping’ MCMC sampler can be viewed as a generalization of the darting method [1]. We illustrate the method on a ‘real world’ vision application of inferring 3D human body pose from single 2-D images.

• Type: Journal
• Year: 2011
• Where: PR
• Authors: Cristian Sminchisescu, Max Welling
• Keywords: Distributed And Parallel Computing, Markov Chain Monte Carlo, MCMC Sampler, PR 2011, Target Distribution