A practical guide to pseudo-marginal methods for computational inference in systems biology

2019/12/28 by David J. Warne, Ruth E. Baker, Matthew J. Simpson · 24 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Algorithm #Approximate Bayesian computation #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Context (archaeology) #Estimation theory #Gaussian Processes and Bayesian Inference #Gene Regulatory Network Analysis #Inference #Likelihood function #Marginal likelihood #Markov Chains and Monte Carlo Methods #Mathematical optimization #Mathematics #Statistical inference #Statistics #msc:62F15 #msc:92C42 #msc:97K80 #q-bio.MN #stat.CO

paper · pdf · doi:10.1016/j.jtbi.2020.110255

published in Journal of Theoretical Biology 496, 110255 (Elsevier BV)

arxiv created 2019/12/28 · openalex publication_date 2020/03/26 · arxiv updated 2021/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable. Likelihood-free computational inference techniques enable parameter inference when the likelihood function for the model is intractable but the generation of many sample paths is feasible through stochastic simulation of the forward problem. The most common likelihood-free method in systems biology is approximate Bayesian computation that accepts parameters that result in low discrepancy between stochastic simulations and measured data. However, it can be difficult to assess how the accuracy of the resulting inferences are affected by the choice of acceptance threshold and discrepancy function. The pseudo-marginal approach is an alternative likelihood-free inference method that utilises a Monte Carlo estimate of the likelihood function. This approach has several advantages, particularly in the context of noisy, partially observed, time-course data typical in biochemical reaction network studies. Specifically, the pseudo-marginal approach facilitates exact inference and uncertainty quantification, and may be efficiently combined with particle filters for low variance, high-accuracy likelihood estimation. In this review, we provide a practical introduction to the pseudo-marginal approach using inference for biochemical reaction networks as a series of case studies. Implementations of key algorithms and examples are provided using the Julia programming language; a high performance, open source programming language for scientific computing.

Citations

• Monte Carlo sampling methods using Markov chains and their applications

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