What exactly is a Bayesian model? - Cross Validated
Dec 14, 2014 · A Bayesian model is a statistical model where you use probability to represent all uncertainty within the model, both the uncertainty regarding the output but also the uncertainty …
What is the best introductory Bayesian statistics textbook?
Which is the best introductory textbook for Bayesian statistics? One book per answer, please.
When are Bayesian methods preferable to 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 …
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 - What is an "uninformative prior"? Can we ever have …
The Bayesian Choice for details.) In an interesting twist, some researchers outside the Bayesian perspective have been developing procedures called confidence distributions that are …
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 …
bayesian - Flat, conjugate, and hyper- priors. What are they?
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 …
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 $\theta$ can a probability …
bayesian - Can someone explain the concept of 'exchangeability ...
The concept is invoked in all sorts of places, and it is especially useful in Bayesian contexts because in those settings we have a prior distribution (our knowledge of the distribution of urns …
bayesian - Low effective sample size but good R-hat is this a …
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 …