Journal
JOURNAL OF STATISTICAL SOFTWARE
Volume 86, Issue 7, Pages 1-33Publisher
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v086.i07
Keywords
Bayesian inference; Markov chain Monte Carlo; Metropolis algorithm; Bayesian computation; nested sampling; C++11; Python
Funding
- Marsden Fast-Start grant from the Royal Society of New Zealand
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available