SUPR
ABC Inference for Multidimensional Diffusions Using Structure-preserving Integrators
Dnr:

NAISS 2024/22-1213

Type:

NAISS Small Compute

Principal Investigator:

Petar Jovanovski

Affiliation:

Chalmers tekniska högskola

Start Date:

2024-09-18

End Date:

2025-10-01

Primary Classification:

10106: Probability Theory and Statistics

Webpage:

Allocation

Abstract

The goal of this project is to develop novel splitting methods for stochastic differential equations (SDE), tailored for use in a simulation-based inference framework to approximate posterior distributions. We additionally employ an invariant neural network, previously developed for Markov processes, to learn low-dimensional representations of SDE solutions. The neural network is incrementally retrained by exploiting an multi-round sampler, which provides new training data at each round