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 …

Abstract Cobaya (Code for BAYesian Analysis) provides a framework for sampling and
statistical modeling and enables exploration of an arbitrary prior or posterior using a range …

We study the benefits and limits of parallelised Markov chain Monte Carlo (MCMC) sampling
in cosmology. MCMC methods are widely used for the estimation of cosmological …

Bayesian parameter inference is an essential tool in modern cosmology, and typically
requires the calculation of 10 5–10 6 theoretical models for each inference of model …

We present a Bayesian sampling algorithm called adaptive importance sampling or
population Monte Carlo (PMC), whose computational workload is easily parallelizable and …

The posterior probability distribution for a set of model parameters encodes all that the data
have to tell us in the context of a given model; it is the fundamental quantity for Bayesian …

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) …

We introduce ScannerBit, the statistics and sampling module of the public, open-source
global fitting framework GAMBIT. ScannerBit provides a standardised interface to different …

We present further development and the first public release of our multimodal nested
sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence …

Given the complexity of modern cosmological parameter inference where we are faced with
non-Gaussian data and noise, correlated systematics and multi-probe correlated datasets …