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Published in:
Recipes for a Good Statistical Analysis Read chapter Read first chapter

2015 | OriginalPaper | Chapter

3. A Bit of Numerical Computation

Authors : Stefano Andreon, Brian Weaver

Published in: Bayesian Methods for the Physical Sciences

Publisher: Springer International Publishing

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Abstract

This chapter introduces the computational tools and methods that we use for sampling from the posterior distribution. Since all numerical computations, and Bayesian ones are no exception, may end in errors, we also provide a few tips to check that the numerical computation is sampling from the posterior distribution.

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previous chapter A Bit of Theory
next chapter Single Parameter Models
Footnotes
1
JAGS (Plummer 2010) can be downloaded from http://​mcmc-jags.​sourceforge.​net/​.
 
2
Skeptical readers are compelled to check that the numerically-sampled posterior is identical to the analytically-derived posterior listed in the exercises.
 
3
This section can be skipped the first time through.
 
4
This section can be skipped the first time through.
 
Literature
F. Feroz, M. P. Hobson, and M. Bridges. MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics. Monthly Notices of the Royal Astronomical Society, 398:1601–1614, 2009.CrossRef
M. Plummer. JAGS Version 2.2.0 user manual, 2010.
M. Plummer, N. Best, K. Cowles, and K. Vines. CODA: convergence diagnosis and output analysis for MCMC. R News, 6:7–11, 2006.
P. Schechter. An analytic expression for the luminosity function for galaxies. The Astrophysical Journal, 203:297–306, 1976.CrossRef
Metadata
Title
A Bit of Numerical Computation
Authors
Stefano Andreon
Brian Weaver
Copyright Year
2015
Book
Bayesian Methods for the Physical Sciences

Print ISBN: 978-3-319-15286-8

Electronic ISBN: 978-3-319-15287-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-15287-5_3

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