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- What exactly is a Bayesian model? - Cross Validated
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
- Posterior Predictive Distributions in Bayesian Statistics
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 Probability vs Bayesian Probability Read part 3: How Bayesian Inference Works in the Context of Science Predictive distributions A predictive distribution is a distribution that we expect for future observations In other
- bayesian - Understanding the Bayes risk - Cross Validated
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 defi
- bayesian - What is an uninformative prior? Can we ever have one with . . .
The Bayesian Choice for details ) In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are probability distributions on the parameter space, constructed by inversion from frequency-based procedures without an explicit prior structure or even a dominating
- bayesian - What are posterior predictive checks and what makes them . . .
I understand what the posterior predictive distribution is, and I have been reading about posterior predictive checks, although it isn't clear to me what it does yet What exactly is the posterior
- bayesian - Why is the Dirichlet distribution the prior for the . . .
@Xi'an's answer (below) helped me - clarifying that the Dirichlet distribution is A prior for the multinomial, not THE prior It's chosen because it is a conjugate prior that works well to describe certain systems such as documents in NLP
- What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please
- r - Understanding Bayesian model outputs - Cross Validated
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 data So, it is our belief about how that parameter is distributed, incorporating information from the prior distribution and from the likelihood (calculated from the data)
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