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 Dirichlet distribution is a conjugate prior for the multinomial distribution. This means that if the prior distribution of the multinomial parameters is Dirichlet then the posterior distribution is also …

Jul 22, 2010 · What is your preferred method of checking for convergence when using Markov chain Monte Carlo for Bayesian inference, and why?

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.

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 …

This is a very simple question but I can't find the derivation anywhere on the internet or in a book. I would like to see the derivation of how one Bayesian updates a multivariate normal distribut...

Feb 25, 2015 · In a Bayesian setting, you can have all of them. Here, parameters are things like the number of clusters; you give this value to the model, and the model considers it a fixed …

Oct 14, 2017 · My understanding is that when using a Bayesian approach to estimate parameter values: The posterior distribution is the combination of the prior distribution and the likelihood …

21 In his widely cited paper Prior distributions for variance parameters in hierarchical models (916 citation so far on Google Scholar) Gelman proposes that good non-informative prior …