Bayesian Inference

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

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