DNest4: Diffusive Nested Sampling in C plus plus and Python

doi:10.18637/jss.v086.i07

DNest4: Diffusive Nested Sampling in C plus plus and Python

2018-01-01

37

OA

AI

B

Brendon J. Brewer *

D

Daniel Foreman-Mackey

DOI：10.18637/jss.v086.i07

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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.

Keyword:

Bayesian inference

Markov chain Monte Carlo

Metropolis algorithm

Bayesian computation

nested sampling

C++11

Python

DNest4: Diffusive Nested Sampling in C plus plus and Python

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