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stat.ME2026
Deep adaptive design with an evidential bias criterion
David Chen, Michael Evans, Xinwei Li +2
Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is…
stat.ME2026
Bayesian Modular Inference for Copula Models with Potentially Misspecified Marginals
Lucas Kock, David T. Frazier, Michael Stanley Smith +1
Copula models of multivariate data are popular because they allow separate specification of marginal distributions and the copula function. These components can be treated as inter…
stat.ME2026
Predictive variational inference for flexible regression models
Lucas Kock, Scott A. Sisson, G. S. Rodrigues +1
A conventional Bayesian approach to prediction uses the posterior distribution to integrate out parameters in a density for unobserved data conditional on the observed data and par…