3 papers
stat.CO2025
Variational inference for steady-state BVARs
Oskar Gustafsson, Mattias Villani
The steady-state Bayesian vector autoregression (BVAR) makes it possible to incorporate prior information about the long-run mean of the process. This has been shown in many studie…
stat.ME2024
Spectral domain likelihoods for Bayesian inference in time-varying parameter models
Oskar Gustafsson, Mattias Villani, Robert Kohn
Inference for locally stationary processes is often based on some local Whittle-type approximation of the likelihood function defined in the frequency domain. The main reasons for…
stat.CO2020
Bayesian Optimization of Hyperparameters from Noisy Marginal Likelihood Estimates
Oskar Gustafsson, Mattias Villani, Pär Stockhammar
Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is a popular iterative method where a Gaussian…