What exactly is a Bayesian model? - Cross Validated
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.
What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please.
Bayesian and frequentist reasoning in plain English
Oct 4, 2011 · How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?
Posterior Predictive Distributions in Bayesian Statistics
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 …
bayesian - Flat, conjugate, and hyper- priors. What are they?
I am currently reading about Bayesian Methods in Computation Molecular Evolution by Yang. In section 5.2 it talks about priors, and specifically Non-informative/flat/vague/diffuse, conjugate, …
Bayesian vs frequentist Interpretations of Probability
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 …
r - Understanding Bayesian model outputs - Cross Validated
Sep 3, 2025 · In a Bayesian framework, we consider parameters to be random variables. The posterior distribution of the parameter is a probability distribution of the parameter given the …
bayesian - What exactly does it mean to and why must one …
Aug 9, 2015 · The point of the Bayesian analysis is to update the prior with the information in the data.
bayesian - Can somebody explain to me NUTS in english? - Cross …
Nov 4, 2017 · You're incorrect that HMC is not a Markov Chain method. Per : In mathematics and physics, the hybrid Monte Carlo algorithm, also known as Hamiltonian Monte Carlo, is a …
bayesian - Can someone explain the concept of 'exchangeability ...
I see the concept of 'exchangeability' being used in different contexts (e.g., bayesian models) but I have never understood the term very well. What does this concept mean? Under what …