|
- 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
- Who Are The Bayesians? - Cross Validated
What distinguish Bayesian statistics is the use of Bayesian models :) Here is my spin on what a Bayesian model is: 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 regarding the input (aka parameters) to the model
- Frequentist vs. Bayesian Probability - Cross Validated
Bayesian probability processing can be combined with a subjectivist, a logical objectivist epistemic, and a frequentist aleatory interpretation of probability, even though there is a strong foundation of subjective probability by de Finetti and Ramsey leading to Bayesian inference, and therefore often subjective probability is identified with
- 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)
- Bayesian and frequentist reasoning in plain English
How would you describe in plain English the characteristics that distinguish Bayesian from Frequentist reasoning?
- Do we believe in existence of true prior distribution in Bayesian . . .
Regarding the Bayesian approach, @Ben has given a good answer Note that there is more than one interpretation of Bayesian probabilities though De Finetti for example is very explicit on not believing in true models and parameters According to him the parametric model is only a device to derive meaningful predictive posterior distributions
- probability - Bayesian Justification of Cross-validation - Cross Validated
Bayesian posterior is uniquely derived from a set of coherency criteria and any other measure is strictly inferior to it (at least when we are only concerned with those coherency criteria) I suppose the usefulness of ELPD and cross-validation is usually manifested when we cannot quantify our priors well enough
|
|
|