3 papers
stat.ME2026
Modeling Dynamic Correlation Matrices with Shrinkage Priors
Daniel Andrew Coulson, David S. Matteson, Martin T. Wells
Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse…
math.ST2026
Minimaxity and Admissibility of Bayesian Neural Networks
Daniel Andrew Coulson, Martin T. Wells
Bayesian neural networks (BNNs) offer a natural probabilistic formulation for inference in deep learning models. Despite their popularity, their optimality has received limited att…
stat.ML2025
Foreclassing: From Forecasts to Decisions
Daniel Andrew Coulson, Martin T. Wells
Time series forecasts are widely used to inform decisions under uncertainty. In many applications, these decisions involve classifying a time series based on outcomes that have yet…