Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models

TL;DR

This paper introduces a novel, automated manifold Hamiltonian Monte Carlo method for efficient Bayesian inference in discretely observed diffusion models, applicable across various system types and observation settings.

Contribution

The authors develop a new manifold HMC approach for diffusion models, leveraging ideas from physics and chemistry, with linear complexity and minimal user intervention.

Findings

Method performs efficiently across different diffusion systems.

Algorithm scales linearly with path discretization and observation count.

Code implementation is publicly available for reproducibility.

Abstract

Bayesian inference for nonlinear diffusions, observed at discrete times, is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology, borrowing ideas from statistical physics and computational chemistry, for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process. Joint configurations of the underlying process noise and of parameters, mapping onto diffusion paths consistent with observations, form an implicitly defined manifold. Then, by making use of a constrained Hamiltonian Monte Carlo algorithm on the embedded manifold, we are able to perform computationally efficient inference for a class of discretely observed diffusion models. Critically, in contrast with other approaches proposed in the literature, our methodology is highly automated, requiring minimal user intervention and applying alike in a range of settings, including: elliptic or hypo-elliptic systems; observations with or without noise; linear or non-linear observation operators. Exploiting Markovianity, we propose a variant of the method with complexity that scales linearly in the resolution of path discretisation and the number of observation times. Python code reproducing the results is available at https://doi.org/10.5281/zenodo.5796148