SNIC SUPR
Efficient inference for stochastic differential mixed-effects models using correlated particle pseudo-marginal algorithms
Dnr:

SNIC 2019/3-630

Type:

SNIC Medium Compute

Principal Investigator:

Umberto Picchini

Affiliation:

Göteborgs universitet

Start Date:

2019-11-22

End Date:

2020-12-01

Primary Classification:

10106: Probability Theory and Statistics

Webpage:

Allocation

Abstract

This project aims at developing efficient Monte-Carlo based inference methods for stochastic differential equation mixed-effects models. Since we are particularly interested in considering "big data" problems we are required to use considerable computational resources and time-consuming simulations.