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 9, 2015 · The point of the Bayesian analysis is to update the prior with the information in the data.

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

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, …

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

Bayesian approaches formulate the problem differently. Instead of saying the parameter simply has one (unknown) true value, a Bayesian method says the parameter's value is fixed but has …

Oct 4, 2011 · How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?

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, …

Sep 3, 2025 · In a Bayesian framework, we consider parameters to be random variables. The posterior distribution of the parameter is a probability distribution of the parameter given the …