A Linear Regression Framework for the Verification of Bayesian Model Calibration Algorithms

Autor:	Nicholas Malaya, Brian J. Williams, Jerry A. McMahan, Ralph C. Smith
Rok vydání:	2017
Předmět:	Statistics and Probability Computational Theory and Mathematics Computer science Calibration (statistics) Modeling and Simulation Linear regression Bayesian inference Algorithm Computer Science Applications
Zdroj:	Journal of Verification, Validation and Uncertainty Quantification. 2
ISSN:	2377-2166 2377-2158
DOI:	10.1115/1.4037705
Popis:	We describe a framework for the verification of Bayesian model calibration routines. The framework is based on linear regression and can be configured to verify calibration to data with a range of observation error characteristics. The framework is designed for efficient implementation and is suitable for verifying code intended for large-scale problems. We propose an approach for using the framework to verify Markov chain Monte Carlo (MCMC) software by combining it with a nonparametric test for distribution equality based on the energy statistic. Our matlab-based reference implementation of the framework is shown to correctly distinguish between output obtained from correctly and incorrectly implemented MCMC routines. Since correctness of output from an MCMC software depends on choosing settings appropriate for the problem-of-interest, our framework can potentially be used for verifying such settings.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9a0bb34884215cd9988fd6a137e5b8b2 https://doi.org/10.1115/1.4037705 Zobrazit plný text záznamu