2019/04/15 by Joshua Hewitt, Hewitt, Joshua, Jennifer A. Hoeting +1
Computer Science · Decision Sciences · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Probabilistic and Robust Engineering Design #Statistical Methods and Bayesian Inference #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.1904.07270
35 pages, 5 figures, 2 tables
arxiv created 2019/04/15 · openalex publication_date 2019/04/15 · arxiv updated 2019/04/17 · openalex created_date 2022/07/29 · openalex updated_date 2026/08/04
We combine conditioning techniques with sparse grid quadrature rules to develop a computationally efficient method to approximate marginal, but not necessarily univariate, posterior quantities, yielding approximate Bayesian inference via Sparse grid Quadrature Evaluation (BISQuE) for hierarchical models. BISQuE reformulates posterior quantities as weighted integrals of conditional quantities, such as densities and expectations. Sparse grid quadrature rules allow computationally efficient approximation of high dimensional integrals, which appear in hierarchical models with many hyperparameters. BISQuE reduces computational effort relative to standard, Markov chain Monte Carlo methods by at least two orders of magnitude on several applied and illustrative models. We also briefly discuss using BISQuE to apply Integrated Nested Laplace Approximations (INLA) to models with more hyperparameters than is currently practical.