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 …

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 …

The Bayesian interpretation of probability as a measure of belief is unfalsifiable. Only if there exists a real-life mechanism by which we can sample values of θ θ can a probability …

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

Bayesian inference is a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability …

Aug 26, 2010 · A particle filter and Kalman filter are both recursive Bayesian estimators. I often encounter Kalman filters in my field, but very rarely see the usage of a particle filter. When …

Oct 14, 2021 · Calculating posterior of difference given posterior of two means How should I compare posterior samples of the same parameter from two Bayesian models? Any …

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. …