7 citations · 15 across the 11 of their papers we have counts for
4 papers · 1 filter
Quantification of observed prior and likelihood information in parametric Bayesian modeling
Giri Gopalan
Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely r…
FastGP: An R Package for Gaussian Processes
Giri Gopalan, Luke Bornn
Despite their promise and ubiquity, Gaussian processes (GPs) can be difficult to use in practice due to the computational impediments of fitting and sampling from them. Here we dis…
Admissibility of a posterior predictive decision rule
Giri Gopalan
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distr…
Classifying X-ray Binaries: A Probabilistic Approach
Giri Gopalan, Saeqa Dil Vrtilek, Luke Bornn
In X-ray binary star systems consisting of a compact object that accretes material from an orbiting secondary star, there is no straightforward means to decide if the compact objec…