JHU BLAST Working Group

Bayesian Learning and Spatio-Temporal modeling
Department of Biostatistics
Johns Hopkins Bloomberg School of Public Health


Photo of Abhi Datta

Abhi Datta

Associate Professor

Department of Biostatistics

Abhi develops statistical and machine learning methods for large spatial datasets as well as Bayesian models for multi-source epidemiological datasets.

Photo of Aki Nishimura

Aki Nishimura

Assistant Professor

Department of Biostatistics

Aki uses Bayesian methods and statistical computing to tackle methodological challenges in healthcare analytics and large-scale biomedical applications.

Upcoming Events

Spectral approaches to speed up Bayesian inference for large stationary time series data

Wednesday, December 7, 2022

Speaker: Dr. Matias Quiroz

This talk will discuss some recent approaches to speed up MCMC for large stationary time series data via data subsampling. We discuss the Whittle log-likelihood for univariate time series and some properties that allow estimating the log-likelihood via data subsampling. We also consider an extension to multivariate time series via the multivariate Whittle log-likelihood and propose a novel model that parsimoniously models semi-long memory properties of multivariate time series.