Bayesian inference for locally stationary processes
Title: Bayesian inference for locally stationary processes
DNr: NAISS 2024/22-829
Project Type: NAISS Small Compute
Principal Investigator: Mattias Villani <>
Affiliation: Stockholms universitet
Duration: 2024-06-05 – 2025-07-01
Classification: 10106


Temporal, spatial and spatiotemporal data are often nonstationary with properties changing over time. This project develops computationally efficient simulation-based methods for Bayesian learning and prediction in locally stationary processes with model for the parameter evolution following recently proposed dynamic shrinkage processes. The models will be developed both in the time and frequency domain, and a particular direction is to develop efficient models for handling time-varying seasonality.