2026 Reading Challenge
Genres

Bayesian


Bayesian Books

Bayesian Data Analysis
Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS
Think Bayes: Bayesian Statistics in Python
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Data Analysis Using Regression and Multilevel/Hierarchical Models
DATA ANALYSIS:BAYESIAN TUTORIAL 2E PAPER: A Bayesian Tutorial
Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
Bayesian Reasoning and Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Bayes Theorem Examples: A Visual Guide For Beginners
Bayesian Computation with R (Use R)
Bayesian Statistics and Marketing (Wiley Series in Probability and Statistics)
Bayesian Analysis with Python: Click here to enter text.
Probabilistic Graphical Models: Principles and Techniques
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Lists

The Oxford Handbook of Rationality by Alfred R. MeleObliquity by John KayUlysses and the Sirens by Jon ElsterRationality in Action by John Rogers SearleThe Limits of Rationality by Karen Schweers Cook
Rationality
100 books — 3 voters
Studies in the history of statistics and probability by E.S. PearsonBayesian Epistemology by Luc BovensLogical Foundations of Probability by Rudolf CarnapMeaning and Necessity by Rudolf CarnapPutting Logic in Its Place by David Christensen
Bayesian Epistemology
37 books — 1 voter

Quotes Tagged “Bayesian”

“ Although Ramsey's and de Finetti's accounts endowed an agent's probabilities with a purely subjective status they knew that, for from rendering those quantities scientifically valueless, the condition of consistency combined with the rule of conditionalization supports a powerful new epistemology called Bayesian epistemology. Its scientific appeal lies principally in two features: (i) so-called Bayesian networks are not only extremely powerful diagnostic tools but also provide the formal basis o ...more ”
― Colin Howson
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“ With Bayesian networks, we had taught machines to think in shades of grey, and this was an important step toward humanlike thinking. But we still couldn't teach machines to understand causes and effects. We couldn't explain to a computer why turning the dial of a barometer won't cause rain.... Without the ability to envision alternate realities and contrast them with the currently existing reality, a machine...cannot answer the most basic question that makes us human: "Why? ...more ”
― Judea Pearl, The Book of Why: The New Science of Cause and Effect
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