Remember that using Bayes' Theorem doesn't make you a Bayesian. Quantifying uncertainty with probability makes you a Bayesian. (Michael Betancourt)
- Books
- Free book on Bayesian Inference written in Jupyter Notebooks - Bayesian Methods for Hackers Cam Davidson-Pilon (the main author)
- Bayesian Data Analysis (2020) Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
- Bayesian Workflow (2020) Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, Martin Modrák
- Introduction to Markov Chain Monte Carlo (2011) Charles Geyer
- MH Metropolis–Hastings algorithm
- Equation of State Calculations by Fast Computing Machines (1953) N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller
- Monte Carlo Sampling Methods Using Markov Chains and Their Applications (1970) W. K. Hastings
- Understanding the Metropolis-Hastings Algorithm (1995) S. Chib, E. Greenberg
- HMC Hamiltonian Monte Carlo
- MCMC Using Hamiltonian Dynamics (2011) Radford M. Neal
- The Geometric Foundations of Hamiltonian Monte Carlo (2014) Michael Betancourt, Simon Byrne, Sam Livingstone, MarkGirolami
- A Conceptual Introduction to Hamiltonian Monte Carlo (2017) Michael Betancourt
- NUTS
- The No-U-Turn Sampler: Adaptively Setting Path Lengthsin Hamiltonian Monte Carlo (2014) Matthew D. Hoffman, Andrew Gelman
- Approximate Bayesian Computation in Population Genetics (2002) Mark A. Beaumont, Wenyang Zhang†and, David J. Balding
- Markov chain Monte Carlo without likelihoods (2003) Paul Marjoram, John Molitor, Vincent Plagnol, Simon Tavare
- Sequential Monte Carlo without likelihoods (2007) S. A. Sisson, Y. Fan†, Mark M. Tanak
- Non-linear regression models for Approximate Bayesian Computation (2009) Michael G.B. Blum, Olivier François
- Likelihood-free Markov chain Monte Carlo (2010) Scott A. Sisson, Yanan Fan
- Approximate Bayesian Computation(ABC) in practice (2010) Katalin Csillery, Michael G.B. Blum, Oscar E. Gaggiotti, Olivier Francois
- Hamiltonian ABC
- Reliable ABC model choice via random forests (2016) Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet, Mathieu Gautier, Christian P. Robert
- Bayesian parameter estimation viavariational methods (1999) T.S. Jaakkola, M.I. Jordan
- The Variational Gaussian Approximation Revisited (2009) Manfred Opper, Cedric Archambeau
- Doubly Stochastic Variational Bayes for non-Conjugate Inference (2014) Michalis K. Titsias, Miguel Lazaro-Gredilla
- Variational Inference: A Review for Statisticians (2018) David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
- SVGD Stein Variational Gradient Descent
- Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm (2019) Qiang Liu, Dilin Wang
- Gaussian Processes for Machine Learning (2006) Carl E. Rasmussen, Christopher K. I. Williams
- The Well-Calibrated Bayesian (1982) A.P. Dawid
- Transforming Classifier Scores into Accurate Multiclass Probability Estimates (2002) Bianca Zadrozny, Charles Elkan
- Predicting Good Probabilities With Supervised Learning (2005) Alexandru Niculescu-Mizil, Rich Caruana
- Nearly-Isotonic Regression (2011) Ryan J. Tibshirani, Holger Hoefling, Robert Tibshirani
- Binary classifier calibration using an ensemble of piecewise linear regression models (2012) Mahdi Pakdaman Naeini, Gregory F. Cooper
- Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers (2017) Meelis Kull, Telmo de Menezes e Silva Filho, Peter Flach
- Verified Uncertainty Calibration (2019) Ananya Kumar, Percy Liang, Tengyu Ma
- Improving Regression Uncertainty Estimates with an Empirical Prior (2020) Eric Zelikman, Christopher Healy
- Stan
- BUGS
- JAGS
- R
- Python
- Julia
- Javascript
- Web
- Installable