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.

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

Jan 7, 2024 · With noninformative priors, however, you deprive yourself of the main advantages of Bayesian modelling. For instance, carefully chosen informative priors expand the space of …

Dec 15, 2014 · The problem is that if you choose non-conjugate priors, you cannot make exact Bayesian inference (simply put, you cannot derive a close-form posterior). Rather, you need to …

Mar 11, 2020 · In Bayesian setting we are dealing with posterior distribution, that is defined in terms of likelihood and priors $$ p (\theta | X) \propto p (X | \theta) \, p (\theta) $$ If you need …

The terminology here is a mess. "Structural equation" is about as vague as "architectural bridge" and "Bayesian network" is not intrinsically Bayesian. Even better, God-of-causality Judea …

"Is Bayesian modeling within probabilistic modeling?" - yes. Frequentist methods for instance are probabilistic methods which are not Bayesian. Bayesian approaches look at posterior …

Mar 17, 2016 · What is the difference between a Bayesian Network and a Markov process? I believed I understood the principles of both, but now when I need to compare the two I feel lost.

Can someone refer me to a good reference that explains the connection between Bayesian statistics and generative modeling techniques? Why do we usually use generative models with …

The basis of all bayesian statistics is Bayes' theorem, which is $$ \mathrm {posterior} \propto \mathrm {prior} \times \mathrm {likelihood} $$ In your case, the likelihood is binomial. If the …