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[ascl:2205.021] CPNest: Parallel nested sampling

CPNest performs Bayesian inference using the nested sampling algorithm. It is designed to be simple for the user to provide a model via a set of parameters, their bounds and a log-likelihood function. An optional log-prior function can be given for non-uniform prior distributions. The nested sampling algorithm is then used to compute the marginal likelihood or evidence. As a by-product the algorithm produces samples from the posterior probability distribution. The implementation is based on an ensemble MCMC sampler which can use multiple cores to parallelize computation.

Code site:
https://github.com/johnveitch/cpnest https://johnveitch.github.io/cpnest/
Used in:
https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.1665K https://ui.adsabs.harvard.edu/abs/2022MNRAS.tmp.1283R
Bibcode:
2022ascl.soft05021D
Preferred citation method:

https://doi.org/10.5281/zenodo.6460935


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ascl:2205.021
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