Title:	Semi-amortised Bayesian inference for hierarchical mixed-effects stochastic models
DNr:	NAISS 2024/22-1324
Project Type:	NAISS Small Compute
Principal Investigator:	Henrik Häggström <henhagg@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2024-10-14 – 2025-04-01
Classification:	10106
Keywords:

Abstract

The goal of this project is to develop computationally efficient Bayesian inference methods for mixed-effects stochastic models, in particular we consider models with time-dynamics, with a focus on stochastic differential equations (SDE). Existing inference methods for these models are computationally intensive, which proves to be a computational bottleneck when the size of the data set increases. We develop a simulation-based inference method training mixture models to provide surrogates of the intractable likelihood and posterior.