cosmocompare: Bayesian model comparison by importance sampling of existing chains
- Author:
Dily Duan Yi Ong
- License:
MIT
- Homepage:
cosmocompare performs Bayesian model comparison without rerunning nested sampling. It takes the posterior samples a survey already produced under one model and prior, reweights them onto a narrowed or changed prior and onto extended models, and recomputes the Bayesian evidence with an uncertainty, so that a model-comparison verdict and its sensitivity to the prior can be obtained in seconds on a laptop.
Developed under a UKRI-funded high performance computing (DiRAC) project.
Installation
pip install cosmocompare # reweighting, evidence, diagnostics
pip install "cosmocompare[likelihood]" # adds the Cobaya likelihood stackRequires Python 3.11 or later.
1 maintainer
Metadata
1 maintainer
Release files for cosmocompare 0.0.12
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