A Hierarchical Sparse Gaussian Process for In Situ Inference in Expensive Physics Simulations
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Rumsey, Kelin Norman
Publications and source records attributed to Rumsey, Kelin Norman.
TBASS: A Robust Adaptation of Bayesian Adaptive Spline Surfaces
The R package TBASS is an extension of the BASS package created by Francom and Sansó (2019). The package is used to fit a Bayesian multivariate adaptive spline to a dataset that either follows a Student’s t-distribution or has outliers. Much of the framework for TBASS is adapted from the concepts of Bayesian Multivariate Adaptive Regression Splines (BMARS), specifically the work done by Denison, Mallick, and Smith (1998). The spline function is fit using a Reversible-Jump Markov Chain Monte Carlo algorithm,. By including this more robust generalization, a dataset with outliers can be accurately fit using the BMARS model, without the possibility of overfitting or variance inflation.
97 MATHEMATICS AND COMPUTING↗
Robust Bayesian Multivariate Adaptive Regression Splines (BMARS) [PowerPoint]
BMARS is described. The aim is to learn where the knots should be placed and how many basis functions to have using Bayesian inference We also want to know if certain knots and/or signs should be added/deleted/changed and the degree of interaction for our basis functions.
97 MATHEMATICS AND COMPUTING↗