3 papers
stat.ME2026
Censored Graphical Horseshoe: Bayesian sparse precision matrix estimation with censored and missing data
The Tien Mai, Sayantan Banerjee
Gaussian graphical models provide a powerful framework for studying conditional dependencies in multivariate data, with widespread applications spanning biomedical, environmental s…
math.ST2025
Bayesian Effective Dimension: A Mutual Information Perspective
Sayantan Banerjee
High-dimensional Bayesian procedures often exhibit behavior that is effectively low dimensional, even when the ambient parameter space is large or infinite-dimensional. This phenom…
stat.ME2025
A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
Sayantan Banerjee, Agnieszka Wylomanska, Sundar S
The autoregressive time series model is a popular second-order stationary process, modeling a wide range of real phenomena. However, in applications, autoregressive signals are oft…