Unbiased Estimation of the Gradient of the Log-Likelihood in Inverse Problems

TL;DR

This paper introduces an unbiased estimator for the gradient of the log-likelihood in Bayesian inverse problems, enabling efficient, parallelizable stochastic gradient algorithms without discretization bias.

Contribution

The authors develop a novel unbiased estimation method for the gradient of the log-likelihood, suitable for parallel computation and applicable to Bayesian inverse problems.

Findings

Estimator is unbiased with finite variance.

Cost to achieve a given error level is comparable to multilevel Monte Carlo.

Allows for parallel computation with fixed, finite time for any precision.

Abstract

We consider the problem of estimating a parameter associated to a Bayesian inverse problem. Treating the unknown initial condition as a nuisance parameter, typically one must resort to a numerical approximation of gradient of the log-likelihood and also adopt a discretization of the problem in space and/or time. We develop a new methodology to unbiasedly estimate the gradient of the log-likelihood with respect to the unknown parameter, i.e. the expectation of the estimate has no discretization bias. Such a property is not only useful for estimation in terms of the original stochastic model of interest, but can be used in stochastic gradient algorithms which benefit from unbiased estimates. Under appropriate assumptions, we prove that our estimator is not only unbiased but of finite variance. In addition, when implemented on a single processor, we show that the cost to achieve a given level of error is comparable to multilevel Monte Carlo methods, both practically and theoretically. However, the new algorithm provides the possibility for parallel computation on arbitrarily many processors without any loss of efficiency, asymptotically. In practice, this means any precision can be achieved in a fixed, finite constant time, provided that enough processors are available.