tag:joss.theoj.org,2005:/papers/tagged/markov%20chain%20monte%20carloJournal of Open Source Software2023-09-28T19:37:54ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46082023-09-28T19:37:54Z2023-09-29T11:54:28ZBlackBIRDS: Black-Box Inference foR Differentiable Simulatorsacceptedv1.02023-07-17 13:26:44 UTC892023-09-28 19:37:54 UTC820235776ArnauQuera-BofarullDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0001-5055-9863JoelDyerDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0002-8304-8450AnisoaraCalinescuDepartment of Computer Science, University of Oxford, UK0000-0003-2082-734XJ.DoyneFarmerInstitute for New Economic Thinking, University of Oxford, UK, Mathematical Institute, University of Oxford, UK, Santa Fe Institute, USA0000-0001-7871-073XMichaelWooldridgeDepartment of Computer Science, University of Oxford, UK0000-0002-9329-841010.21105/joss.05776https://doi.org/10.5281/zenodo.8377044Pythonhttps://joss.theoj.org/papers/10.21105/joss.05776.pdfBayesian inference, differentiable simulators, variational inference, Markov chain Monte Carlotag:joss.theoj.org,2005:Paper/44502023-07-26T13:23:53Z2023-07-27T00:01:44ZPxMCMC: A Python package for proximal Markov Chain Monte Carloacceptedv0.1.52023-04-24 05:20:17 UTC872023-07-26 13:23:53 UTC820235582AugustinMarignierResearch School of Earth Sciences, Australian National University, Australia0000-0001-6778-139910.21105/joss.05582https://doi.org/10.5281/zenodo.8185139Pythonhttps://joss.theoj.org/papers/10.21105/joss.05582.pdfMCMC, imaging, geophysics, astrophysicstag:joss.theoj.org,2005:Paper/32932022-04-18T15:53:10Z2022-04-19T00:00:54ZSGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithmsaccepted0.2.92022-01-17 18:25:10 UTC722022-04-18 15:53:10 UTC720224113JeremieCoullonCervest, London, UK0000-0002-7032-3425ChristopherNemethLancaster University, UK0000-0002-9084-386610.21105/joss.04113https://doi.org/10.5281/zenodo.6460681Pythonhttps://joss.theoj.org/papers/10.21105/joss.04113.pdfJAX, MCMC, SGMCMC, Bayesian inferencetag:joss.theoj.org,2005:Paper/31172022-01-14T14:04:16Z2022-01-15T00:01:27ZcompareMCMCs: An R package for studying MCMC efficiencyaccepted0.5.02021-10-18 20:34:22 UTC692022-01-14 14:04:16 UTC720223844Perryde ValpineUniversity of California, BerkeleySallyPaganinUniversity of California, BerkeleyDanielTurekWilliams College10.21105/joss.03844https://doi.org/10.5281/zenodo.5842623Rhttps://joss.theoj.org/papers/10.21105/joss.03844.pdfstatistics, Markov chain Monte Carlo, nimble, JAGStag:joss.theoj.org,2005:Paper/20472021-05-13T13:24:09Z2021-05-20T20:10:30ZParaMonte: A high-performance serial/parallel Monte Carlo simulation library for C, C++, Fortranacceptedv1.2.02020-09-29 21:20:03 UTC612021-05-13 13:24:09 UTC620212741AmirShahmoradiDepartment of Physics, The University of Texas, Arlington, TX, Data Science Program, The University of Texas, Arlington, TX0000-0002-9720-8937FatemehBagheriDepartment of Physics, The University of Texas, Arlington, TX10.21105/joss.02741https://doi.org/10.5281/zenodo.4749957Python, Fortranhttps://joss.theoj.org/papers/10.21105/joss.02741.pdfC, C++, Monte Carlo, Markov Chain Monte Carlo, Uncertainty Quantification, Metropolis-Hastings, adaptive sampling, MCMC, DRAMtag:joss.theoj.org,2005:Paper/9752019-07-09T11:38:18Z2021-02-15T11:32:15Zfmcmc: A friendly MCMC frameworkaccepted0.1-02019-04-23 18:15:40 UTC392019-07-09 11:38:18 UTC420191427GeorgeG VegaYonDepartment of Preventive Medicine, University of Southern California0000-0002-3171-0844PaulMarjoramDepartment of Preventive Medicine, University of Southern California0000-0003-0824-744910.21105/joss.01427https://doi.org/10.5281/zenodo.3272759R, C++https://joss.theoj.org/papers/10.21105/joss.01427.pdfmetropolis-hastings, mcmc, markov chain monte carlo, transition kernel, automatic convergencetag:joss.theoj.org,2005:Paper/502017-03-20T00:00:00Z2021-02-15T11:34:27Zwalkr: MCMC Sampling from Non-Negative Convex Polytopesacceptedv0.3.32016-09-12 01:29:21 UTC112017-03-20 00:00:00 UTC2201761AndyYu ZhuYaoWilliams College0000-0003-3898-8782DavidKaneIQSS, Harvard University10.21105/joss.00061https://doi.org/10.5281/zenodo.400944R, C++https://joss.theoj.org/papers/10.21105/joss.00061.pdfMonte Carlo Markov Chain, sampling, random walks, convex polytope