- Referenced in 554 articles
- BUGS project, which aims to make practical MCMC methods available to applied statisticians. WinBUGS...
- Referenced in 222 articles
- only possible using Markov Chain Monte Carlo (MCMC) techniques. The preeminent experts in the field ... aspects, construct fast and reliable algorithms for MCMC inference, and provide an online C-library...
- Referenced in 294 articles
- statistical models using Markov chain Monte Carlo (MCMC) methods. The project began...
- Referenced in 188 articles
- package coda: Output analysis and diagnostics for MCMC , Output analysis and diagnostics for Markov Chain ... output from Markov Chain Monte Carlo (MCMC) simulations, as well as diagnostic tests of convergence...
- Referenced in 251 articles
- involves computationally intensive Markov chain Monte Carlo (MCMC) methods whose efficiency depends upon the specific...
- Referenced in 124 articles
- hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS...
- Referenced in 92 articles
- language implementing full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood...
- Referenced in 82 articles
- likelihood estimation and Markov Chain Monte Carlo (MCMC) methods. MLwiN is based on an earlier...
- Referenced in 62 articles
- expert system’, which determines an appropriate MCMC (Markov chain Monte Carlo) scheme (based...
- Referenced in 37 articles
- MCMCpack: Markov chain Monte Carlo (MCMC) Package. This package contains functions to perform Bayesian inference ... Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using...
- Referenced in 50 articles
- accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively...
- Referenced in 30 articles
- package rjags: Bayesian graphical models using MCMC. Interface to the JAGS MCMC library. The rjags ... analysis. JAGS uses Markov Chain Monte Carlo (MCMC) to generate a sequence of dependent samples...
- Referenced in 24 articles
- approaches such as Markov chain Monte Carlo (MCMC) methods are often infeasible or ineffective ... from gene expression cancer genomics, comparisons with MCMC and other methods, and theoretical and simulation...
- Referenced in 31 articles
- models. MrBayes uses Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution...
- Referenced in 30 articles
- BEAST 2 uses Markov chain Monte Carlo (MCMC) to average over tree space, so that...
- Referenced in 20 articles
- BRugs: R interface to the OpenBUGS MCMC software. Fully-interactive R interface to the OpenBUGS ... software for Bayesian analysis using MCMC sampling. Runs natively and stably...
- Referenced in 17 articles
- package MCMCglmm: MCMC Generalised Linear Mixed Models. MCMC Generalised Linear Mixed Models...
- Referenced in 16 articles
- Implementation of the ’t-walk’ MCMC Algorithm. The ’t-walk’ is a general-purpose MCMC...
- Referenced in 22 articles
- simple structure(s), (3) tailored for MCMC calculations within GMRF. (4) S3 and S4 like...
- Referenced in 13 articles
- emcee: The MCMC Hammer. We introduce a stable, well tested Python implementation of the affine ... ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code ... behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance...