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[ascl:2103.022] nestle: Nested sampling algorithms for evaluating Bayesian evidence

nestle is a pure Python implementation of nested sampling algorithms for evaluating Bayesian evidence. Nested sampling integrates posterior probability in order to compare models in Bayesian statistics. It is similar to Markov Chain Monte Carlo (MCMC) in that it generates samples that can be used to estimate the posterior probability distribution. Unlike MCMC, the nature of the sampling also allows one to calculate the integral of the distribution. It is also a pretty good method for robustly finding global maxima.

Code site:
https://github.com/kbarbary/nestle http://kylebarbary.com/nestle/
Used in:
https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.1199F https://ui.adsabs.harvard.edu/abs/2021arXiv210308600W
Bibcode:
2021ascl.soft03022B
Preferred citation method:

Please see citation information here: http://kylebarbary.com/nestle/#citation


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ascl:2103.022
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