Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy Markov chain Monte Carlobased Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the past decade, we have also seen a steady increase in the number of papers that employ Monte Carlobased Bayesian analysis. New, efficient Monte Carlobased methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlobased methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (https:github.comsanjibsbmcmc) Python that implements some of the algorithms and examples discussed here. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Astronomy and Astrophysics Annual Reviews

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

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Publisher
Annual Reviews
Copyright
Copyright 2017 by Annual Reviews. All rights reserved
ISSN
0066-4146
eISSN
1545-4282
D.O.I.
10.1146/annurev-astro-082214-122339
Publisher site
See Article on Publisher Site

Abstract

Markov chain Monte Carlobased Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the past decade, we have also seen a steady increase in the number of papers that employ Monte Carlobased Bayesian analysis. New, efficient Monte Carlobased methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlobased methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (https:github.comsanjibsbmcmc) Python that implements some of the algorithms and examples discussed here.

Journal

Annual Review of Astronomy and AstrophysicsAnnual Reviews

Published: Aug 18, 2017

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