Hacker News with Generative AI: Bayesian Statistics

It is time to stop teaching frequentism to non-statisticians (2012) (arxiv.org)
We should cease teaching frequentist statistics to undergraduates and switch to Bayes. Doing so will reduce the amount of confusion and over-certainty rife among users of statistics.
Frequentism and Bayesianism: A Practical Introduction (2014) (jakevdp.github.io)
One of the first things a scientist hears about statistics is that there is are two different approaches: frequentism and Bayesianism. Despite their importance, many scientific researchers never have opportunity to learn the distinctions between them and the different practical approaches that result. The purpose of this post is to synthesize the philosophical and pragmatic aspects of the frequentist and Bayesian approaches, so that scientists like myself might be better prepared to understand the types of data analysis people do.
Bayesian Modeling and Computation in Python (2021) (bayesiancomputationbook.com)
Bayesian Epistemology (2022) (plato.stanford.edu)
We can think of belief as an all-or-nothing affair. For example, I believe that I am alive, and I don’t believe that I am a historian of the Mongol Empire. However, often we want to make distinctions between how strongly we believe or disbelieve something.
Any Interest in Reading Group for Jaynes Probability Theory the Logic of Science (ycombinator.com)
Jaynes' Probability Theory the Logic of Science is considered one of the most important books for Bayesian statistics for 20th century.
Which books, papers, and blogs are in the Bayesian canon? (stat.columbia.edu)
Objective Bayesian Hypothesis Testing (objectivebayesian.com)
Bayesian Statistics: The three cultures (stat.columbia.edu)