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[ascl:2207.018] pocoMC: Preconditioned Monte Carlo method for accelerated Bayesian inference

pocoMC performs Bayesian inference, including model comparison, for challenging scientific problems. The code utilizes a normalizing flow to precondition the target distribution by removing any correlations between its parameters. pocoMC then generates posterior samples, used for parameter estimation, with a powerful adaptive Sequential Monte Carlo algorithm manifesting a sampling efficiency that can be orders of magnitude higher than without precondition. Furthermore, pocoMC also provides an unbiased estimate of the model evidence that can be used for the task of Bayesian model comparison. The code is designed to excel in demanding parameter estimation problems that include multimodal and highly non–Gaussian target distributions.

Code site:
https://github.com/minaskar/pocomc
Described in:
https://ui.adsabs.harvard.edu/abs/2022arXiv220705652K
Bibcode:
2022ascl.soft07018K

Views: 1239

ascl:2207.018
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