Dec 14, 2014 · Bayesian Analysis, 1(1):1-40. there are 2 answers: Your model is first Bayesian if it uses Bayes' rule (that's the "algorithm"). More broadly, if you infer (hidden) causes from a …

Aug 14, 2015 · What distinguish Bayesian statistics is the use of Bayesian models :) Here is my spin on what a Bayesian model is: A Bayesian model is a statistical model where you use …

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: …

Jul 30, 2013 · Today, Gelman argues against the automatic choice of non-informative priors, saying in Bayesian Data Analysis that the description "non-informative" reflects his attitude …

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 …

Bayesian probability frames problems in e.g. statistics in quite a different way, which the other answers discuss. The Bayesian system seems to be a direct application of the theory of …

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

My bayesian-guru professor from Carnegie Mellon agrees with me on this. having the minimum knowledge of statistics and R and Bugs(as the easy way to DO something with Bayesian stat) …

I'm trying to fit a Bayesian model. This model has many parameters (about 150). I run MCMC (10000 iters) with a thinning period to avoid correlation problem and with the hope it would …

Aug 10, 2017 · In Bayesian analysis, a lot of the densities we come up with aren't analytically tractable: you can only integrate them -- if you can integrate them at all -- with a great deal of …