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.

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 …

Jun 17, 2014 · 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 …

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 …

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

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 …

Jul 18, 2019 · I am using Stan (Hamiltonian Monte-Carlo) to run a highly paramaterized model. One of the parameters in particular has a very low effective sample size (n_eff < .10*number …

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 …

Jan 14, 2017 · How do we go about calculating a posterior with a prior N~(a, b) after observing n data points? I assume that we have to calculate the sample mean and variance of the data …