A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Related tags

Machine Learning awesome-bayesian-statistics
Overview

Awesome Bayesian Statistics

This is a repository that I created while learning Bayesian Statistics. It contains links to resources such as books, articles, magazines, research papers, and influential people in the domain of Bayesian Statistics. It will be helpful for beginners who want a one-stop access to all the resources at one place.

It is a collaborative work, so feel free to pull and add content to this. This way, we will be able to make it more community-driven.

Books

  1. Bayesian Statistics for Beginners: A Step-by-Step Approach, Therese M. Donovan (2019)
  2. Doing Bayesian Data Analysis: A Tutorial Introduction with R, John Kruschke (2010)
  3. Introduction to Bayesian Statistics, William M. Bolstad (2004)
  4. Bayesian Data Analysis, Donald Rubin (1995)
  5. Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks, Will Kurt (2019)
  6. A First Course in Bayesian Statistical Methods, Peter D Hoff (2009)
  7. Think Bayes: Bayesian Statistics in Python, Allen B. Downey (2012)
  8. A Student's Guide to Bayesian Statistics, Ben Lambert (2018)
  9. Bayesian Analysis with Python: Introduction to Statistical Modelling and Probabilistic Programming using PyMC3 and ArviZ, Osvaldo Martin (2016)
  10. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, Cameron Davidson-Pilon (2015)
  11. The Bayesian Way: Introduction Statistics for Economists and Engineers, Svein Olav Nyberg (2018)
  12. Bayesian Biostatistics, Emmanuel Lesaffre (2012)
  13. Bayes Theorem: A Visual Introduction for Beginners, Dan Morris (2017)
  14. Bayesian Econometrics, Gary Koop (2003)
  15. Regression Modelling with Spatial and Spatial-Temporal Data: A Bayesian Approach, Robert P. Haining (2019)
  16. Bayesian Reasoning and Machine Learning, David Barber (2012)

Courses

  1. Bayesian Statistics: From Concept to Data Analysis, University of California Santa Cruz
  2. Bayesian Methods for Machine Learning, HSE University
  3. Introduction to Bayesian Analysis Course with Python 2021, Udemy
  4. Bayesian Machine Learning in Python: A/B Testing, Udemy
  5. A Comprehensive Guide to Bayesian Statistics, Udemy
  6. Statistical Rethinking, Max Planck Institute for Evolutionary Anthropology, Leipzig
  7. Bayesian Statistics for the Social Science, Benjamin Goodrich, Columbia University New York
  8. Bayesian Data Analysis in Python, Datacamp

Curriculum and Syllabus

  1. MATH 574 Bayesian Computational Statistics, Illinois Tech
  2. STAT 695 - Bayesian Data Analysis, Purdue University
  3. STA360/601 - Bayesian Inference and Modern Statistical Methods, Duke University
  4. STAT 625: Advanced Bayesian Inference, Rice
  5. MSH3 - Advanced Bayesian Inference, University of Sydney

Blogs

  1. Count Bayesie by Will Kurt
  2. Evan Miller
  3. Healthy Algorithms
  4. Allen Downey
  5. Statistics Biophysics Blog
  6. Statistical Thinking by Frank Harrell
  7. Bayesian Statistics and Functional Programming
  8. Learning Bayesian Statistics

Web Articles

  1. Absolutely the simplest introduction to Bayesian statistics
  2. My Journey From Frequentist to Bayesian Statistics
  3. Frequentist vs. Bayesian approach in A/B testing
  4. Bayesian vs. Frequentist A/B Testing: What’s the Difference?
  5. Bayesian inference tutorial: a hello world example
  6. Nonparametric Bayesian Statistics
  7. A Guide to Bayesian Statistics
  8. Bayesian Priors for Parameter Estimation
  9. Bayesian Statistics Wikipedia
  10. Bayes’ Theorem: the maths tool we probably use every day, but what is it?
  11. Develop an Intuition for Bayes Theorem With Worked Examples
  12. Bayes Theorem, mathisfun.com
  13. Is Bayes' Theorem really that interesting?
  14. Understand Bayes’ Theorem Through Visualization
  15. Bayes's Theorem: What's the Big Deal?
  16. Bayes Theorem: A Framework for Critical Thinking
  17. Why testing positive for a disease may not mean you are sick. Visualization of the Bayes Theorem and Conditional Probability
  18. How To Use Bayes's Theorem In Real Life
  19. A Gentle Introduction to Markov Chain Monte Carlo for Probability
  20. Markov Chain Monte Carlo Without all the Bullshit
  21. How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
  22. Markov Chain Monte Carlo in Practice
  23. Causal Bayesian Networks: A flexible tool to enable fairer machine learning
  24. A Comprehensive Introduction to Bayesian Deep Learning
  25. A Technical Explanation of Technical Explanation
  26. An Intuitive Explanation of Bayes Theorem

Research Papers

  1. Primer on the Use of Bayesian Methods in Health Economics
  2. Experimental Design: Bayesian Designs
  3. A simple introduction to Markov Chain Monte-Carlo sampling
  4. Markov Chain Monte Carlo: an introduction for epidemiologists
  5. Monte Carlo simulation of climate systems
  6. What Are Hierarchical Models and How Do We Analyze Them?
  7. A Conceptual Introduction to Markov Chain Monte Carlo Methods
  8. Data Analysis Recipes: Using Markov Chain Monte Carlo
  9. A survey of Monte Carlo methods for parameter estimation
  10. Uncertain Neighbors: Bayesian Propensity Score Matching For Causal Inference
  11. Bayesian Matching for Causal Inference
  12. A Bayesian Approach for Estimating Causal Effects from Observational Data
  13. Bayesian Nonpar esian Nonparametric Methods F ametric Methods For Causal Inf or Causal Inference And ence And Prediction
  14. Is Microfinance Truly Useless for Poverty Reduction and Women Empowerment? A Bayesian Spatial-Propensity Score Matching Evaluation in Bolivia
  15. Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
  16. State-of-the-BART: Simple Bayesian Tree Algorithms for Prediction and Causal Inference

People

  1. Andreas Krause, Professor of Computer Science, ETH Zurich
  2. Svetha Venkatesh, Professor of Computer Science, Deakin University
  3. Juergen Branke, Professor of Operational Research and Systems, Warwick Business School
  4. Michael A Osborne, Professor of Machine Learning, University of Oxford
  5. Matthias Seeger, Principal Applied Scientist, Amazon
  6. Eytan Bakshy, Research Director, Facebook
  7. Aaron Klein, AWS Research Berlin
  8. David Ginsbourger,University of Bern
  9. Jonathan Marchini, Head of Statistical Genetics and Methods, Regeneron Genetics Center
  10. Kyle Foreman, University of Washington
  11. Adrian E. Raftery, Professor of Statistics and Sociology, University of Washington
  12. Zoubin Ghahramani, Professor, University of Cambridge, and Distinguished Researcher, Google
  13. Jun S Liu, Professor of statistics, Harvard University
  14. David Dunson, Arts & Sciences Professor of Statistical Science & Mathematics, Duke
  15. Giovanni Parmigiani, Professor Department of Data Science, DFCI
  16. Aki Vehtari, Associate Professor, Aalto University
  17. Chiara Sabatti, Professor of Biomedical Data Science and of Statistics, Stanford University
  18. Peter E Rossi, James Collins Professor of Economics, Marketing, and Statistics, UCLA
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Comments
  • YouTube - Towards Bayesian Regression & Inference

    YouTube - Towards Bayesian Regression & Inference

    Hi @aayushmalik

    Many thanks for this collection. This is a really good compilation.

    Here is the link to the playlist of videos on my YouTube channel on this subject. It is still a work in progress and would be publishing many more in this series.

    https://youtube.com/playlist?list=PLivJwLo9VCUISiuiRsbm5xalMbIwOHOOn

    If you think it is suitable to be included in this repo please feel free to add it.

    Regards Kapil

    opened by ksachdeva 1
Owner
Aayush Malik
Aayush Malik
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