# Cobaya: code for Bayesian analysis of hierarchical physical models

@article{Torrado2020CobayaCF,
title={Cobaya: code for Bayesian analysis of hierarchical physical models},
journal={Journal of Cosmology and Astroparticle Physics},
year={2020},
volume={2021}
}
• Published 2020
• Physics, Computer Science
• Journal of Cosmology and Astroparticle Physics
We present , a general-purpose Bayesian analysis code aimed at models with complex internal interdependencies. Without the need for specific code by the user, interdependencies between different stages of a model pipeline are exploited for sampling efficiency: intermediate results are automatically cached, and parameters are grouped in blocks according to their dependencies and optimally sorted, taking into account their individual computational costs, so as to minimize the cost of their… Expand
Accelerating MCMC algorithms through Bayesian Deep Networks
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#### References

SHOWING 1-10 OF 68 REFERENCES
GetDist: a Python package for analysing Monte Carlo samples
Methods used the Python GetDist package to calculate marginalized one and two dimensional densities using Kernel Density Estimation (KDE) to calculate convergence diagnostics and produces tables of limits and output in latex. Expand
MontePython 3: Boosted MCMC sampler and other features
• Physics
• Physics of the Dark Universe
• 2019
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 bothExpand
Conservative constraints on early cosmology with MONTE PYTHON
• Physics
• 2013
Models for the latest stages of the cosmological evolution rely on a less solid theoretical and observational ground than the description of earlier stages like BBN and recombination. As suggested inExpand
Inference from Iterative Simulation Using Multiple Sequences
• Mathematics
• 1992
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterativeExpand
Nested sampling for general Bayesian computation
Nested sampling estimates directly how the likelihood function relates to prior mass. The evidence (alternatively the marginal likelihood, marginal den- sity of the data, or the prior predictive) isExpand
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
SUMMARY A generalization of the sampling method introduced by Metropolis et al. (1953) is presented along with an exposition of the relevant theory, techniques of application and methods andExpand
Cosmological parameters from CMB and other data: A Monte Carlo approach
• Physics
• 2002
We present a fast Markov chain Monte Carlo exploration of cosmological parameter space. We perform a joint analysis of results from recent cosmic microwave background ~CMB! experiments and provideExpand
polychord: nested sampling for cosmology
• Physics, Computer Science
• 2015
PolyChord is a novel nested sampling algorithm tailored for high dimensional parameter spaces that utilises slice sampling at each iteration to sample within the hard likelihood constraint of nested sampling. Expand
CosmoSIS: Modular cosmological parameter estimation
This paper presents a new framework for cosmological parameter estimation, CosmoSIS, designed to connect together, share, and advance development of inference tools across the community, including CAMB, Planck, cosmic shear calculations, and a suite of samplers. Expand
Core Cosmology Library: Precision Cosmological Predictions for LSST
The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests.Expand