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MontePython 3: Parameter inference code for cosmology

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Ada Coda
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MontePython 3: Parameter inference code for cosmology

Postby Ada Coda » Wed May 30, 2018 6:33 pm

MontePython 3: Parameter inference code for cosmology

Abstract: MontePython 3 provides numerous ways to explore parameter space using Monte Carlo Markov Chain (MCMC) sampling, including Metropolis-Hastings, Nested Sampling, Cosmo Hammer, and a Fisher sampling method. This improved version of the Monte Python (ascl:1307.002) parameter inference code for cosmology offers new ingredients that improve the performance of Metropolis-Hastings sampling, speeding up convergence and offering significant time improvement in difficult runs. Additional likelihoods and plotting options are available, as are post-processing algorithms such as Importance Sampling and Adding Derived Parameter.

Credit: Brinckmann, Thejs; Lesgourgues, Julien; Audren, Benjamin; Benabed, Karim; Prunet, Simon

Site: https://github.com/brinckmann/montepython_public
http://adsabs.harvard.edu/abs/2018arXiv180407261B

Bibcode: 2018ascl.soft05027B

Preferred citation method: http://adsabs.harvard.edu/abs/2018arXiv180407261B and http://adsabs.harvard.edu/abs/2013JCAP...02..001A

ID: ascl:1805.027
Last edited by Ada Coda on Fri Jun 01, 2018 8:55 pm, edited 1 time in total.
Reason: Updated code entry.

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