MontePython 3: Boosted MCMC sampler and other features

By T. Brinckmann, Julien Lesgourgues. Physics of the Dark Universe, 2018

636 citations recorded by the source corpus.

MontePython is a parameter inference package for cosmology. We present the latest development of the code over the past couple of years. We explain, in particular, two new ingredients both contributing to improve the performance of Metropolis-Hastings sampling: an adaptation algorithm for the jumping factor, and a calculation of the inverse Fisher matrix, which can be used as a proposal density. We present several examples to show that these features speed up convergence and can save many hundreds of CPU-hours in the case of difficult runs, with a poor prior knowledge of the covariance matrix. We also summarise all the functionalities of MontePython in the current release, including new likelihoods and plotting options.

Subfields: Astronomy and Astrophysics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b1bd63d9e776dd221e40298ddba121b9999ebf84

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Citations in the corpus

Early Dark Energy can Resolve the Hubble Tension. (2018, methodology): “We run a Markov Chain Monte Carlo (MCMC) using the public code MontePython-v3 [45, 46] and a modified version of the CLASScode [47, 48].”
Rock ‘n’ roll solutions to the Hubble tension (2019, methodology): “We perform parameter scans over fφ,MC and ln (1 + zc,MC) as well as the standard six flat ΛCDM parameters using the MontePython [38, 39] software package version 3.”
H0LiCOW – XIII. A 2.4 per cent measurement of H0 from lensed quasars: 5.3σ tension between early- and late-Universe probes (2019, neutral): “1 MCMC sampling package (Audren et al. 2013; Brinckmann & Lesgourgues 2018), its associated CLASS code (Lesgourgues 2011), and the JLA and Pantheon SNe samples implemented within MONTEPYTHON, to combine with our lensing distance measurements and sample cosmological parameters.”
2020 global reassessment of the neutrino oscillation picture (2020, methodology): “The analysis is performed using MontePython [78, 79] for the computation of the likelihoods and running the Markov Chain Monte Carlo (MCMC) simulations.”
Cosmological parameters from the BOSS galaxy power spectrum (2019, methodology): “The parameter constraints are obtained with the April 2018 version of the Montepython code [45, 81].”
Effective field theory of dark energy: A review (2019, background): “hi class is interfaced with MontePython [148, 149] to compute cosmological constraints.”
The Hubble tension in light of the Full-Shape analysis of Large-Scale Structure data (2020, methodology): “10 The posteriors were sampled using MontePython cosmological parameter inference code [84, 85].”
Combining full-shape and BAO analyses of galaxy power spectra: a 1.6% CMB-independent constraint on H0 (2020, neutral): “0 [73, 74] to optimize for α = {α‖, α⊥}, with the additional nuisance parameters {b,ΣNL} of Sec.”
The BAO+BBN take on the Hubble tension (2019, methodology): “ect of neutrino drag in the minimal CDM model is consistently taken into account. The approximation r BAO(z) = r s(z D) appears only at the level of BAO likelihoods in sampling codes like MontePython [7,42] or CosmoMC [55]. In these codes the quantity { 13 { computed for each model and tted to the d”
Nonlinear perturbation theory extension of the Boltzmann code CLASS (2020, methodology): “Additionally, we publicly release custom-built BOSS galaxy power spectrum likelihoods written for the Markov Chain Monte Carlo sampler Montepython [6, 12], 1The commonly used Boltzmann codes do have nonlinear modules featuring fitting formulas like HALOFIT [11].”
BOSS DR12 full-shape cosmology: ΛCDM constraints from the large-scale galaxy power spectrum and bispectrum monopole (2021, methodology): “To compute the parameter posteriors, we employ a Markov Chain Monte Carlo (MCMC) analysis, implemented within the Montepython code [90].”