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 …

In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are probability distributions on the parameter …

I am currently reading about Bayesian Methods in Computation Molecular Evolution by Yang. In section 5.2 it talks about priors, and specifically Non-informative/flat/vague/diffuse, conjugate, …

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

Oct 15, 2017 · 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 …

Oct 9, 2024 · I am interested in the parameter estimates (estimated simultaneously) I would get when either X1 = X2 X 1 = X 2 (collinearity), or when cor(X1,X2) cor (X 1, X 2) is very high, …

Feb 5, 2020 · There is no distinction between discrete and continuous when you move to a measure-theoretic view of integration, which makes questions like this impossible to ask (not a …

I see the concept of 'exchangeability' being used in different contexts (e.g., bayesian models) but I have never understood the term very well. What does this concept mean? Under what …

Feb 9, 2017 · As pointed out in the answers, the question is probabilistically meaningless because of the integration of random variables on one side that are the conditioning variables on the …