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…
stat.ML2026
Foreclassing: A new machine learning perspective on human decision making with temporal data
Daniel Andrew Coulson, Martin T. Wells
Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and uncertainty about future outcomes, and…
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…