Dec 14, 2014 · A Bayesian model is just a model that draws its inferences from the posterior distribution, i.e. utilizes a prior distribution and a likelihood which are related by Bayes' theorem.

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 …

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

The Bayesian, on the other hand, think that we start with some assumption about the parameters (even if unknowingly) and use the data to refine our opinion about those parameters. Both are …

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

Bayesian inference is not a component of deep learning, even though the later may borrow some Bayesian concepts, so it is not a surprise if terminology and symbols differ. However, if you …

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, …

Jan 30, 2015 · I understand what the posterior predictive distribution is, and I have been reading about posterior predictive checks, although it isn't clear to me what it does yet. What exactly is …

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. …

Oct 9, 2024 · However, if I estimate the regression model (using a Bayesian model in the fully colinear case, or Bayesian/Frequentist for a near colinear case) I get beta coefficients which …