tag:joss.theoj.org,2005:/papers/tagged/Bayesian%20InferenceJournal of Open Source Software2024-02-07T22:25:26ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/46792024-02-07T22:25:26Z2024-02-12T11:55:04ZChi: A Python package for treatment response modellingacceptedv0.2.32023-08-06 13:35:22 UTC942024-02-07 22:25:26 UTC920245925DavidAugustinDepartment of Computer Science, University of Oxford, Oxford, United Kingdom0000-0002-4885-108810.21105/joss.05925https://doi.org/10.5281/zenodo.10510572Pythonhttps://joss.theoj.org/papers/10.21105/joss.05925.pdfpkpd, treatment planning, inference, Bayesian inferencetag: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/45242023-09-26T10:32:27Z2023-10-24T13:30:07ZBernadette: Bayesian Inference and Model Selection for Stochastic Epidemics in Racceptedv.1.1.42023-06-04 21:22:05 UTC892023-09-26 10:32:27 UTC820235612LamprosBouranisDepartment of Statistics, Athens University of Economics and Business, Athens, Greece0000-0002-1291-219210.21105/joss.05612https://doi.org/10.5281/zenodo.8376673R, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.05612.pdfBayesian, Epidemicstag:joss.theoj.org,2005:Paper/45702023-09-22T03:40:40Z2023-09-25T08:21:56ZBayesFlow: Amortized Bayesian Workflows With Neural Networksacceptedv1.1.12023-06-27 14:58:24 UTC892023-09-22 03:40:40 UTC820235702StefanT.RadevCluster of Excellence STRUCTURES, Heidelberg University, Germany0000-0002-6702-9559MarvinSchmittCluster of Excellence SimTech, University of Stuttgart, Germany0000-0003-1293-820XLukasSchumacherInstitute for Psychology, Heidelberg University, Germany0000-0003-1512-8288LasseElsemüllerInstitute for Psychology, Heidelberg University, Germany0000-0003-0368-720XValentinPratzVisual Learning Lab, Heidelberg University, Germany0000-0001-8371-3417YannikSchälteLife and Medical Sciences Institute, University of Bonn, Germany0000-0003-1293-820XUllrichKötheVisual Learning Lab, Heidelberg University, Germany0000-0001-6036-1287Paul-ChristianBürknerCluster of Excellence SimTech, University of Stuttgart, Germany, Department of Statistics, TU Dortmund University, Germany0000-0001-5765-899510.21105/joss.05702https://doi.org/10.5281/zenodo.8346393Pythonhttps://joss.theoj.org/papers/10.21105/joss.05702.pdfsimulation-based inference, likelihood-free inference, Bayesian inference, amortized Bayesian inferencetag:joss.theoj.org,2005:Paper/45312023-09-15T22:14:43Z2023-09-16T00:01:22Zpysersic: A Python package for determining galaxy structural properties via Bayesian inference, accelerated with jaxacceptedv0.1.12023-06-07 14:31:39 UTC892023-09-15 22:14:43 UTC820235703ImadPashaDepartment of Astronomy, Yale University, USA, National Science Foundation Graduate Research Fellow0000-0002-7075-9931TimB.MillerDepartment of Astronomy, Yale University, USA0000-0001-8367-626510.21105/joss.05703https://doi.org/10.5281/zenodo.8335352https://joss.theoj.org/papers/10.21105/joss.05703.pdfPython, astronomy, galaxies, model fittingtag:joss.theoj.org,2005:Paper/43142023-06-21T01:25:57Z2023-06-24T19:34:40ZPyVBMC: Efficient Bayesian inference in Pythonacceptedv1.0.02023-03-16 14:20:45 UTC862023-06-21 01:25:57 UTC820235428BobbyHugginsUniversity of Helsinki0009-0006-3475-5964ChengkunLiUniversity of Helsinki0000-0001-5848-910XMarlonTobabenUniversity of Helsinki0000-0002-9778-0853MikkoJ.AarnosUniversity of HelsinkiLuigiAcerbiUniversity of Helsinki0000-0001-7471-733610.21105/joss.05428https://doi.org/10.5281/zenodo.7966315Pythonhttps://joss.theoj.org/papers/10.21105/joss.05428.pdfBayesian statistics, Bayesian inference, Probabilistic programming, Model evidence, Machine learning, Simulator-based inferencetag:joss.theoj.org,2005:Paper/41442023-04-20T14:54:21Z2023-04-21T00:03:22ZRxInfer: A Julia package for reactive real-time Bayesian inferenceacceptedv2.4.12023-01-03 13:07:23 UTC842023-04-20 14:54:21 UTC820235161DmitryBagaevTechnical University of Eindhoven0000-0001-9655-7986AlbertPodusenkoTechnical University of Eindhoven0000-0003-0515-0465Bertde VriesTechnical University of Eindhoven0000-0003-0839-174X10.21105/joss.05161https://doi.org/10.5281/zenodo.7774921Jupyter Notebook, Juliahttps://joss.theoj.org/papers/10.21105/joss.05161.pdfstatistics, Bayesian inference, variational optimization, message passingtag:joss.theoj.org,2005:Paper/39952023-03-09T21:00:11Z2023-03-10T00:04:43ZflowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAXacceptedv0.0.72022-11-10 08:41:32 UTC832023-03-09 21:00:11 UTC820235021KazeW. k.WongCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0001-8432-7788MarylouGabriéÉcole Polytechnique, Palaiseau 91120, France, Center for Computational Mathematics, Flatiron Institute, New York, NY 10010, US0000-0002-5989-1018DanielForeman-MackeyCenter for Computational Astrophysics, Flatiron Institute, New York, NY 10010, US0000-0002-9328-565210.21105/joss.05021https://doi.org/10.5281/zenodo.7706605Pythonhttps://joss.theoj.org/papers/10.21105/joss.05021.pdfBayesian Inference, Machine Learning, JAXtag:joss.theoj.org,2005:Paper/38482022-12-26T14:37:35Z2022-12-28T19:01:29ZMParT: Monotone Parameterization Toolkitaccepted1.0.02022-08-30 22:22:24 UTC802022-12-26 14:37:35 UTC720224843MatthewParnoDartmouth College, Hanover, NH USA, Solea Energy, Overland Park, KS USA0000-0002-9419-2693Paul-BaptisteRubioMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-9765-1162DanielSharpMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-0439-5084MichaelBrennanMassachusetts Institute of Technology, Cambridge, MA USA0000-0001-7812-9347RicardoBaptistaMassachusetts Institute of Technology, Cambridge, MA USA0000-0002-0421-890XHenningBonartMassachusetts Institute of Technology, Cambridge, MA USA, Technische Universität Darmstadt, Darmstadt, Germany0000-0002-5026-4499YoussefMarzoukMassachusetts Institute of Technology, Cambridge, MA USA0000-0001-8242-329010.21105/joss.04843https://doi.org/10.5281/zenodo.7435142C++, Objective-C, MATLABhttps://joss.theoj.org/papers/10.21105/joss.04843.pdfPython, Julia, measure transport, transport map, density estimation, Bayesian inference, normalizing flows, machine learningtag:joss.theoj.org,2005:Paper/37192022-11-16T16:19:19Z2022-11-17T00:01:38Zgeostan: An R package for Bayesian spatial analysisacceptedV0.3.02022-07-14 01:08:25 UTC792022-11-16 16:19:19 UTC720224716ConnorDoneganGeography and Geospatial Information Sciences, The University of Texas at Dallas, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center0000-0002-9698-544310.21105/joss.04716https://doi.org/10.5281/zenodo.7311716R, C++, Stanhttps://joss.theoj.org/papers/10.21105/joss.04716.pdfspatial data, survey data, Bayesian inference