A Common Derivation for Markov Chain Monte Carlo Algorithms with Tractable and Intractable Targets

TL;DR

This paper introduces a unified framework for various Markov chain Monte Carlo algorithms, enabling new combinations and enhancements, including novel slice sampling schemes that work with intractable targets.

Contribution

A generalized derivation of MCMC algorithms using a random generator and self-reverse mapping, unifying diverse methods and enabling novel algorithm combinations.

Findings

Unified derivation connects multiple MCMC algorithms.

Proposed two new sampling schemes combining slice and Hamiltonian sampling.

Hamiltonian slice sampling applicable with intractable targets via unbiased estimation.

Abstract

Markov chain Monte Carlo is a class of algorithms for drawing Markovian samples from high-dimensional target densities to approximate the numerical integration associated with computing statistical expectation, especially in Bayesian statistics. However, many Markov chain Monte Carlo algorithms do not seem to share the same theoretical support and each algorithm is proven in a different way. This incurs many terminologies and ancillary concepts, which makes Markov chain Monte Carlo literature seems to be scattered and intimidating to researchers from many other fields, including new researchers of Bayesian statistics. A generalised version of the Metropolis-Hastings algorithm is constructed with a random number generator and a self-reverse mapping. This formulation admits many other Markov chain Monte Carlo algorithms as special cases. A common derivation for many Markov chain Monte Carlo algorithms is useful in drawing connections and comparisons between these algorithms. As a result, we now can construct many novel combinations of multiple Markov chain Monte Carlo algorithms that amplify the efficiency of each individual algorithm. Specifically, we propose two novel sampling schemes that combine slice sampling with directional or Hamiltonian sampling. Our Hamiltonian slice sampling scheme is also applicable in the pseudo-marginal context where the target density is intractable but can be unbiasedly estimated, e.g. using particle filtering.