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 from Information Growth
Sayantan Banerjee
Nominal dimension can greatly overstate how much a Bayesian model actually learns from data. We quantify this gap through a prior-dependent effective dimension based on parameter--…
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…