Editing Markov chain Monte Carlo (section)

== Software ==
Several software programs provide MCMC sampling capabilities, for example:
* [https://github.com/cdslaborg/paramonte ParaMonte] parallel Monte Carlo software available in multiple programming languages including [[C (programming language)|C]], [[C++]], [[Fortran]], [[MATLAB]], and [[Python (programming language)|Python]].
* Packages that use dialects of the [[Bayesian inference using Gibbs sampling|BUGS]] model language:
** [[WinBUGS]] / [[OpenBUGS]]/ [https://www.multibugs.org/ MultiBUGS]
** [[Just another Gibbs sampler|JAGS]]
* [[MCSim]]
* Julia language with packages like 
** [https://turing.ml/ Turing.jl]
** [https://juliahub.com/ui/Packages/General/DynamicHMC/ DynamicHMC.jl]
** [https://github.com/madsjulia/AffineInvariantMCMC.jl AffineInvariantMCMC.jl]
** [https://github.com/probcomp/Gen.jl Gen.jl]
** and the ones in StanJulia repository. 
* [[Python (programming language)]] with the packages:   
** [https://blackjax-devs.github.io/blackjax/ Blackjax].
** [https://emcee.readthedocs.io/en/stable/ emcee],<ref>{{Cite journal |last1=Foreman-Mackey |first1=Daniel |last2=Hogg |first2=David W. |last3=Lang |first3=Dustin |last4=Goodman |first4=Jonathan |date=2013-11-25 |title=emcee: The MCMC Hammer |journal=Publications of the Astronomical Society of the Pacific |volume=125 |issue=925 |pages=306–312 |doi=10.1086/670067|arxiv=1202.3665 |bibcode=2013PASP..125..306F |s2cid=88518555 }}</ref> 
** [https://num.pyro.ai/en/stable/getting_started.html NumPyro]<ref>{{cite arXiv |last1=Phan |first1=Du |title=Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro |date=2019-12-24 |eprint=1912.11554 |last2=Pradhan |first2=Neeraj |last3=Jankowiak |first3=Martin|class=stat.ML }}</ref> 
** [[PyMC]]
* [[R (programming language)]] with the packages adaptMCMC, atmcmc, BRugs, mcmc, MCMCpack, ramcmc, rjags, rstan, etc.
* [[Stan (software)|Stan]]
* [https://www.tensorflow.org/probability/ TensorFlow Probability] ([[Probabilistic programming language|probabilistic programming]] library built on [[TensorFlow]])
* [https://www.cse-lab.ethz.ch/korali/ Korali] high-performance framework for Bayesian UQ, optimization, and reinforcement learning.
* [https://causascientia.org/software/MacMCMC/MacMCMC.html MacMCMC] — Full-featured application (freeware) for MacOS, with advanced functionality, available at [https://causascientia.org causaScientia]