Efficient statistical inference for stochastic reaction processes

TL;DR

This paper introduces an efficient approximation method for statistical inference in stochastic reaction processes, enabling parameter estimation and state inference from sparse, noisy data, even with unobserved variables.

Contribution

The authors develop a novel asymptotic system size expansion approach for inference in stochastic reaction models, extending to cases with unobserved variables.

Findings

Validates the method on model systems

Effective with sparse, noisy measurements

Handles partially observed systems

Abstract

We address the problem of estimating unknown model parameters and state variables in stochastic reaction processes when only sparse and noisy measurements are available. Using an asymptotic system size expansion for the backward equation we derive an efficient approximation for this problem. We demonstrate the validity of our approach on model systems and generalize our method to the case when some state variables are not observed.