Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Brendon J. Brewer, Daniel Foreman-Mackey
Title: DNest4: Diffusive Nested Sampling in C++ and Python
Abstract: In probabilistic (Bayesian) inferences, we typically want to compute properties of the posterior distribution, describing knowledge of unknown quantities in the context of a particular dataset and the assumed prior information. The marginal likelihood, also known as the "evidence", is a key quantity in Bayesian model selection. The diffusive nested sampling algorithm, a variant of nested sampling, is a powerful tool for generating posterior samples and estimating marginal likelihoods. It is effective at solving complex problems including many where the posterior distribution is multimodal or has strong dependencies between variables. DNest4 is an open source (MIT licensed), multi-threaded implementation of this algorithm in C++11, along with associated utilities including: (i) 'RJObject', a class template for finite mixture models; and (ii) a Python package allowing basic use without C++ coding. In this paper we demonstrate DNest4 usage through examples including simple Bayesian data analysis, finite mixture models, and approximate Bayesian computation.

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Paper: DNest4: Diffusive Nested Sampling in C++ and Python     Download PDF (Downloads: 832)
DNest4-0.2.3.tar.gz: R source package Download (Downloads: 83; 653KB) Python replication code Download (Downloads: 105; 894B)

DOI: 10.18637/jss.v086.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.