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.

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 …

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 large range of …

The Bayesian landscape When we setup a Bayesian inference problem with N N unknowns, we are implicitly creating a N N dimensional space for the prior distributions to exist in. Associated …

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

Nov 4, 2017 · You're incorrect that HMC is not a Markov Chain method. Per Wikipedia: In mathematics and physics, the hybrid Monte Carlo algorithm, also known as Hamiltonian Monte …

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 …

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

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 …

I am looking for uninformative priors for beta distribution to work with a binomial process (Hit/Miss). At first I thought about using $\\alpha=1, \\beta=1$ that generate an uniform PDF, or …