# Modified Hamiltonian Monte Carlo for Bayesian inference

## TL;DR

This paper introduces MMHMC, an improved Hamiltonian Monte Carlo method that incorporates importance sampling and irreversibility, significantly enhancing sampling efficiency especially in high-dimensional Bayesian inference tasks.

## Contribution

The paper proposes MMHMC, a novel generalized HMC method that combines importance sampling, partial momentum refreshment, and modified Hamiltonians for better sampling performance.

## Key findings

- MMHMC outperforms traditional HMC and other methods in efficiency.
- The method is especially effective in high-dimensional problems.
- A new metric for comparing sampling methods was proposed.

## Abstract

The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computational statistics. We show that performance of HMC can be significantly improved by incorporating importance sampling and an irreversible part of the dynamics into a chain. This is achieved by replacing Hamiltonians in the Metropolis test with modified Hamiltonians, and a complete momentum update with a partial momentum refreshment. We call the resulting generalized HMC importance sampler---Mix & Match Hamiltonian Monte Carlo (MMHMC). The method is irreversible by construction and further benefits from (i) the efficient algorithms for computation of modified Hamiltonians; (ii) the implicit momentum update procedure and (iii) the multi-stage splitting integrators specially derived for the methods sampling with modified Hamiltonians. MMHMC has been implemented, tested on the popular statistical models and compared in sampling efficiency with HMC, Riemann Manifold Hamiltonian Monte Carlo, Generalized Hybrid Monte Carlo, Generalized Shadow Hybrid Monte Carlo, Metropolis Adjusted Langevin Algorithm and Random Walk Metropolis-Hastings. To make a fair comparison, we propose a metric that accounts for correlations among samples and weights, and can be readily used for all methods which generate such samples. The experiments reveal the superiority of MMHMC over popular sampling techniques, especially in solving high dimensional problems.