  The Markov Chain Monte Carlo (MCMC) algorithm is a widely recognised as an
efficient method for sampling a specified posterior distribution. However, when
the posterior is multi-modal, conventional MCMC algorithms either tend to
become stuck in one local mode, become non-Markovian or require an excessively
long time to explore the global properties of the distribution. We propose a
novel variant of MCMC, mixed MCMC, which exploits a specially designed proposal
density to allow the generation candidate points from any of a number of
different modes. This new method is efficient by design, and is strictly
Markovian. We present our method and apply it to a toy model inference problem
to demonstrate its validity.
