%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 }