3 papers
stat.ME2026
Non-parametric Bayesian inference via loss functions under model misspecification
Yu Luo, David A. Stephens, Daniel J. Graham +1
In the usual Bayesian setting, a full probabilistic model is required to link the data and parameters, and the form of this model and the inference and prediction mechanisms are sp…
stat.ML2025
Generalized Random Forests using Fixed-Point Trees
David Fleischer, David A. Stephens, Archer Y. Yang
We propose a computationally efficient alternative to generalized random forests (GRFs) for estimating heterogeneous effects in large dimensions. While GRFs rely on a gradient-base…
stat.ME2024
Bayesian Analysis of Sigmoidal Gaussian Cox Processes via Data Augmentation
Renaud Alie, David A. Stephens, Alexandra M. Schmidt
Many models for point process data are defined through a thinning procedure where locations of a base process (often Poisson) are either kept (observed) or discarded (thinned). In…