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

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

When evaluating an estimator, the two probably most common used criteria are the maximum risk and the Bayes risk. My question refers to the latter one: The bayes risk under the prior $\\pi$ is …

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 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 $\theta$ can a probability …

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 …

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 …