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BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.
Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
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Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
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Hybrid Monte Carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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A generalized guided Monte Carlo algorithm
Alan M Horowitz · 1991
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Simulated tempering: A new Monte Carlo scheme
E. Marinari and G. Parisi · 1992
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Comments on “Representations of knowledge in complex systems” by U. Grenander and M. I. Miller
Julian Besag · 1994
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Annealing Markov chain Monte Carlo with applications to ancestral inference
Charles J Geyer and Elizabeth A Thompson · 1995
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An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility
David J Lunn, Andrew Thomas, Nicky Best, and David Spiegelhalter · 2000
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BUGS for a Bayesian analysis of stochastic volatility models
Renate Meyer and Jun Yu · 2000
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Sequential Monte Carlo samplers
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra · 2006
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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
Håvard Rue, Sara Martino, and Nicolas Chopin · 2009
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The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman, Andrew Gelman, et al · 2014
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Identifying the optimal integration time in Hamiltonian Monte Carlo, 2016
Michael Betancourt · 2016
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Just Another Gibbs Sampler (JAGS): Flexible software for MCMC implementation
Sarah Depaoli, James P. Clifton, and Patrice R. Cobb · 2016
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The Metropolis–Hastings algorithm, 2016
Christian P. Robert · 2016
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Stan: A probabilistic programming language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus A Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Pyro: Deep universal probabilistic programming
Eli Bingham, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D Goodman · 2019
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EMCEE v3: A Python ensemble sampling toolkit for affine-invariant MCMC
Daniel Foreman-Mackey, Will M Farr, Manodeep Sinha, Anne M Archibald, David W Hogg, Jeremy S Sanders, Joe Zuntz, Peter KG Williams, Andrew RJ Nelson, Miguel de Val-Borro, et al · 2019
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Arviz a unified library for exploratory analysis of bayesian models in python
Ravin Kumar, Colin Carroll, Ari Hartikainen, and Osvaldo Martin · 2019
Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
Aymeric Galan, Georgios Vernardos, Austin Peel, Frédéric Courbin, and J-L Starck · 2022
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Tuning-free generalized Hamiltonian Monte Carlo
Matthew D Hoffman and Pavel Sountsov · 2022
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pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology
Minas Karamanis, David Nabergoj, Florian Beutler, John A Peacock, and Uros Seljak · 2022
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Probabilistic Machine Learning: An introduction
Kevin P. Murphy · 2022
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GPJax: A Gaussian process framework in JAX
Thomas Pinder and Daniel Dodd · 2022
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Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
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Composable effects for flexible and accelerated probabilistic programming in NumPyro
Du Phan, Neeraj Pradhan, and Martin Jankowiak · 2019
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Bayesian Layers: A module for neural network uncertainty
Dustin Tran, Michael W. Dusenberry, Danijar Hafner, and Mark van der Wilk · 2019
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The DeepMind JAX Ecosystem, 2020
DeepMind, Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Antoine Dedieu, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, George Papamakarios, John Quan, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Laurent Sartran, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Miloš Stanojević, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola · 2020
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Unbiased Markov chain Monte Carlo methods with couplings
Pierre E Jacob, John O’Leary, and Yves F Atchadé · 2020
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tfp.mcmc: Modern Markov chain Monte Carlo tools built for modern hardware, 2020
Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, and Joshua V. Dillon · 2020
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Pure Happiness with Pure Functions, 2020
Brian Lonsdorf · 2020
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Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions
Radford M Neal · 2020
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Brian Staber and Sébastien Da Veiga · 2022
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Non-reversible parallel tempering: a scalable highly parallel MCMC scheme
Saifuddin Syed, Alexandre Bouchard-Côté, George Deligiannidis, and Arnaud Doucet · 2022
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PyMC: a modern, and comprehensive probabilistic programming framework in Python
Oriol Abril-Pla, Virgile Andreani, Colin Carroll, Larry Dong, Christopher J Fonnesbeck, Maxim Kochurov, Ravin Kumar, Junpeng Lao, Christian C Luhmann, Osvaldo A Martin, et al · 2023
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Jumanji: a diverse suite of scalable reinforcement learning environments in JAX, 2023
Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Vincent Coyette, Paul Duckworth, Laurence I. Midgley, Tristan Kalloniatis, Sasha Abramowitz, Cemlyn N. Waters, Andries P. Smit, Nathan Grinsztajn, Ulrich A. Mbou Sob, Omayma Mahjoub, Elshadai Tegegn, Mohamed A. Mimouni, Raphael Boige, Ruan de Kock, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, and Alexandre Laterre · 2023
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Transport elliptical slice sampling
Alberto Cabezas and Christopher Nemeth · 2023
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Bayesian cross-validation by parallel Markov chain Monte Carlo
Alex Cooper, Aki Vehtari, Catherine Forbes, Lauren Kennedy, and Dan Simpson · 2023
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PyMC: A modern and comprehensive probabilistic programming framework in Python
Abril-Pla Oriol, Andreani Virgile, Carroll Colin, Dong Larry, Fonnesbeck Christopher J., Kochurov Maxim, Kumar Ravin, Lao Jupeng, Luhmann Christian C., Martin Osvaldo A., Osthege Michael, Vieira Ricardo, Wiecki Thomas, and Zinkov Robert · 2023
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Bayes–Newton methods for approximate Bayesian inference with PSD guarantees
William J Wilkinson, Simo Särkkä, and Arno Solin · 2023
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CANDL: Cosmic microwave background analysis with a differentiable likelihood
L Balkenhol, C Trendafilova, K Benabed, and S Galli · 2024
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Adrian M Price-Whelan, Jason AS Hunt, Danny Horta, Micah Oeur, David W Hogg, Kathryn V Johnston, and Lawrence Widrow · 2024
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