Dec 14, 2014 · A Bayesian model is a statistical model made of the pair prior x likelihood = posterior x marginal. Bayes' theorem is somewhat secondary to the concept of a prior.

Feb 17, 2021 · Confessions of a moderate Bayesian, part 4 Bayesian statistics by and for non-statisticians Read part 1: How to Get Started with Bayesian Statistics Read part 2: Frequentist …

Aug 14, 2015 · What distinguish Bayesian statistics is the use of Bayesian models :) Here is my spin on what a Bayesian model is: A Bayesian model is a statistical model where you use …

Jul 30, 2013 · Flat priors have a long history in Bayesian analysis, stretching back to Bayes and Laplace. A "vague" prior is highly diffuse though not necessarily flat, and it expresses that a …

The Bayesian Choice for details.) In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are …

Jun 17, 2014 · People do use Bayesian techniques for regression. But because the frequentist methods are very convenient and many people are pragmatic about which approach they use, …

Which is the best introductory textbook for Bayesian statistics? One book per answer, please.

The Bayesian interpretation of probability as a measure of belief is unfalsifiable. Only if there exists a real-life mechanism by which we can sample values of θ θ can a probability …

5 Bayesian Estimation Supersedes the t-Test for John K. Kruschke is one of the most important papers that I had read explaining how to run the Bayesian analysis and how to make the plots. …

When evaluating an estimator, the two probably most common used criteria are the maximum risk and the Bayes risk. My question refers to the latter one: The bayes risk under the prior $\\pi$ is …