Cobaya: Code for Bayesian Analysis of hierarchical physical models. We present Cobaya, 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 variation during sampling, thanks to a novel algorithm. Cobaya allows exploration of 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. 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 can exploit 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.
References in zbMATH (referenced in 2 articles )
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- Bernardo, Reginald Christian; Said, Jackson Levi: A data-driven reconstruction of Horndeski gravity via the Gaussian processes (2021)
- Hogg, N. B.; Martinelli, M.; Nesseris, S.: Constraints on the distance duality relation with standard sirens (2020)