Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology

Abstract: We propose a novel methodology for Bayesian inference in hierarchical mixed-effects models. We construct a framework that is highly scalable, where amortized approximations to the likelihood and the parameters posterior are first obtained, and these are rapidly refined for each individual dataset, to ultimately approximate the parameters posterior across many individuals. The framework introduced is easily trainable, as it uses mixture of experts but without neural networks, leading to parsimonious yet expressive surrogate models of the likelihood and the posterior. The methodology is exemplified via stochastic differential equation mixed-effects models, that are highly relevant in systems biology, but the methodology is general and can accommodate other types of stochastic and deterministic models. We compare our approximate inference with exact pseudomarginal inference and show that our methodology is fast and competitive.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.