Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

TL;DR

This paper investigates ensemble inference methods for complex models with noisy and costly likelihood evaluations, comparing Kalman and Langevin approaches, and introduces ensemble Gaussian process samplers that combine their advantages.

Contribution

It provides a multiscale analysis of particle system algorithms under noisy conditions and proposes a novel ensemble Gaussian process sampler that improves inference accuracy.

Findings

Ensemble Kalman methods perform well with noisy likelihoods.

Langevin methods are accurate in noise-free settings but affected by noise.

Ensemble Gaussian process samplers outperform existing methods.

Abstract

The increasing availability of data presents an opportunity to calibrate unknown parameters which appear in complex models of phenomena in the biomedical, physical and social sciences. However, model complexity often leads to parameter-to-data maps which are expensive to evaluate and are only available through noisy approximations. This paper is concerned with the use of interacting particle systems for the solution of the resulting inverse problems for parameters. Of particular interest is the case where the available forward model evaluations are subject to rapid fluctuations, in parameter space, superimposed on the smoothly varying large scale parametric structure of interest. {A motivating example from climate science is presented, and ensemble Kalman methods (which do not use the derivative of the parameter-to-data map) are shown, empirically, to perform well. Multiscale analysis is then used to analyze the behaviour of interacting particle system algorithms when rapid fluctuations, which we refer to as noise, pollute the large scale parametric dependence of the parameter-to-data map. Ensemble Kalman methods and Langevin-based methods} (the latter use the derivative of the parameter-to-data map) are compared in this light. The ensemble Kalman methods are shown to behave favourably in the presence of noise in the parameter-to-data map, whereas Langevin methods are adversely affected. On the other hand, Langevin methods have the correct equilibrium distribution in the setting of noise-free forward models, whilst ensemble Kalman methods only provide an uncontrolled approximation, except in the linear case. Therefore a new class of algorithms, ensemble Gaussian process samplers, which combine the benefits of both ensemble Kalman and Langevin methods, are introduced and shown to perform favourably.