Cobaya: Code for Bayesian Analysis of hierarchical physical models
J. Torrado, and A. Lewis. (2020)cite arxiv:2005.05290Comment: 12 pages, 4 figures.
Abstract
We present Cobaya, a general-purpose Bayesian analysis code aimed at models
with complex internal interdependencies. It allows exploration of arbitrary
posteriors using a range of Monte Carlo samplers, and also has functions for
maximization and importance-reweighting of Monte Carlo samples with new priors
and likelihoods. Interdependencies of the different stages of a model pipeline
and their individual computational costs are automatically exploited for
sampling efficiency, cacheing intermediate results when possible and optimally
grouping parameters in blocks, which are sorted so as to minimize the cost of
their variation. Cobaya is written in Python in a modular way that allows for
extendability, use of calculations provided by external packages, and dynamical
reparameterization without modifying its source. It exploits hybrid OpenMP/MPI
parallelization, and has sub-millisecond overhead per posterior evaluation.
Though Cobaya is a general purpose statistical framework, it includes
interfaces to a set of cosmological Boltzmann codes and likelihoods (the latter
being agnostic with respect to the choice of the former), and automatic
installers for external dependencies.
Description
Cobaya: Code for Bayesian Analysis of hierarchical physical models
%0 Generic
%1 torrado2020cobaya
%A Torrado, Jesus
%A Lewis, Antony
%D 2020
%K tifr
%T Cobaya: Code for Bayesian Analysis of hierarchical physical models
%U http://arxiv.org/abs/2005.05290
%X We present Cobaya, a general-purpose Bayesian analysis code aimed at models
with complex internal interdependencies. It allows exploration of arbitrary
posteriors using a range of Monte Carlo samplers, and also has functions for
maximization and importance-reweighting of Monte Carlo samples with new priors
and likelihoods. Interdependencies of the different stages of a model pipeline
and their individual computational costs are automatically exploited for
sampling efficiency, cacheing intermediate results when possible and optimally
grouping parameters in blocks, which are sorted so as to minimize the cost of
their variation. Cobaya is written in Python in a modular way that allows for
extendability, use of calculations provided by external packages, and dynamical
reparameterization without modifying its source. It exploits hybrid OpenMP/MPI
parallelization, and has sub-millisecond overhead per posterior evaluation.
Though Cobaya is a general purpose statistical framework, it includes
interfaces to a set of cosmological Boltzmann codes and likelihoods (the latter
being agnostic with respect to the choice of the former), and automatic
installers for external dependencies.
@misc{torrado2020cobaya,
abstract = {We present Cobaya, a general-purpose Bayesian analysis code aimed at models
with complex internal interdependencies. It allows exploration of arbitrary
posteriors using a range of Monte Carlo samplers, and also has functions for
maximization and importance-reweighting of Monte Carlo samples with new priors
and likelihoods. Interdependencies of the different stages of a model pipeline
and their individual computational costs are automatically exploited for
sampling efficiency, cacheing intermediate results when possible and optimally
grouping parameters in blocks, which are sorted so as to minimize the cost of
their variation. Cobaya is written in Python in a modular way that allows for
extendability, use of calculations provided by external packages, and dynamical
reparameterization without modifying its source. It exploits hybrid OpenMP/MPI
parallelization, and has sub-millisecond overhead per posterior evaluation.
Though Cobaya is a general purpose statistical framework, it includes
interfaces to a set of cosmological Boltzmann codes and likelihoods (the latter
being agnostic with respect to the choice of the former), and automatic
installers for external dependencies.},
added-at = {2020-05-12T07:54:52.000+0200},
author = {Torrado, Jesus and Lewis, Antony},
biburl = {https://www.bibsonomy.org/bibtex/213972fcc04bcd2f1f46196a3cff3eda7/citekhatri},
description = {Cobaya: Code for Bayesian Analysis of hierarchical physical models},
interhash = {e8593849d65b818fd6a1de20db34789c},
intrahash = {13972fcc04bcd2f1f46196a3cff3eda7},
keywords = {tifr},
note = {cite arxiv:2005.05290Comment: 12 pages, 4 figures},
timestamp = {2020-05-12T07:54:52.000+0200},
title = {Cobaya: Code for Bayesian Analysis of hierarchical physical models},
url = {http://arxiv.org/abs/2005.05290},
year = 2020
}