Related papers: SED fitting with MCMC: methodology and application…

We apply Monte Carlo Markov Chain (MCMC) methods to large-scale simulations of galaxy formation in a LambdaCDM cosmology in order to explore how star formation and feedback are constrained by the observed luminosity and stellar mass…

We investigate the ability of the Croton et al. (2006) semi-analytic model to reproduce the evolution of observed galaxies across the final 7 billion years of cosmic history. Using Monte-Carlo Markov Chain techniques we explore the…

Markov Chain Monte Carlo (MCMC) proves to be powerful for Bayesian inference and in particular for exoplanet radial velocity fitting because MCMC provides more statistical information and makes better use of data than common approaches like…

Stellar clusters are regularly used to study the evolution of their host galaxy. Except for a few nearby galaxies, these studies rely on the interpretation of integrated cluster properties, especially integrated photometry observed using…

We present a new methodology to generate mock halo or galaxy catalogues, which have accurate clustering properties, nearly indistinguishable from full $N$-body solutions, in terms of the one-point, two-point, and three-point statistics. In…

We implement a sample-efficient method for rapid and accurate emulation of semi-analytical galaxy formation models over a wide range of model outputs. We use ensembled deep learning algorithms to produce a fast emulator of an updated…

Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference. However, these algorithms include hyperparameters such as step size or batch size that influence the accuracy of…

For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare…

This paper illustrates how mock observational samples of high-redshift galaxies with sophisticated selection criteria can be extracted from the predictions of GALICS, a hybrid model of hierarchical galaxy formation that couples the outputs…

Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters. It uses a particle filter (PF) at each iteration of an…

We present MCSED, a new spectral energy distribution (SED)-fitting code, which mates flexible stellar evolution calculations with the Markov Chain Monte Carlo algorithms of the software package emcee. MCSED takes broad, intermediate, and…

We introduce a new Markov-Chain Monte Carlo (MCMC) approach designed for efficient sampling of highly correlated and multimodal posteriors. Parallel tempering, though effective, is a costly technique for sampling such posteriors. Our…

In the case of a linear state space model, we implement an MCMC sampler with two phases. In the learning phase, a self-tuning sampler is used to learn the parameter mean and covariance structure. In the estimation phase, the parameter mean…

Context. Synthetic model atmosphere calculations are still the most commonly used tool when determining precise stellar parameters and stellar chemical compositions. Besides three-dimensional models that consistently solve for hydrodynamic…

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and…

Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target distribution with a "surrogate" (i.e. an approximate and…

We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational…

Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's…

Multiwavelength observations are now the norm for studying blazars' various states of activity, classifying them, and determining possible underlying physical processes driving their emission. Broadband emission models became unavoidable…

We present single-S\'ersic two-dimensional model fits to 167,600 galaxies modelled independently in the ugrizYJHK bandpasses using reprocessed Sloan Digital Sky Survey Data Release Seven (SDSS DR7) and UKIRT Infrared Deep Sky Survey Large…