|
- Posterior Predictive Distributions in Bayesian Statistics - Physics Forums
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
- bayesian - Flat, conjugate, and hyper- priors. What are they? - Cross . . .
Today, Gelman argues against the automatic choice of non-informative priors, saying in Bayesian Data Analysis that the description "non-informative" reflects his attitude towards the prior, rather than any "special" mathematical features of the prior (Moreover, there was a question in the early literature of at what scale a prior is
- mathematical statistics - 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
- 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 trying to develop a model which can explain the observations and make predictions; the difference is in the assumptions (both actual and philosophical)
- What is the best introductory Bayesian statistics textbook?
My bayesian-guru professor from Carnegie Mellon agrees with me on this having the minimum knowledge of statistics and R and Bugs(as the easy way to DO something with Bayesian stat) Doing Bayesian Data Analysis: A Tutorial with R and BUGS is an amazing start You can compare all offered books easily by their book cover!
- Bayesian vs frequentist Interpretations of Probability
Bayesian probability frames problems in e g statistics in quite a different way, which the other answers discuss The Bayesian system seems to be a direct application of the theory of probability, which seeks to avoid inferring anything which is not already known, and only inferring based on exactly what has been observed
- bayesian - What is an uninformative prior? Can we ever have one with . . .
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 measure on this parameter space
- Newest bayesian Questions - Cross Validated
Bayesian inference is a method of statistical inference that relies on treating the model parameters as random variables and applying Bayes' theorem to deduce subjective probability statements about the parameters or hypotheses, conditional on the observed dataset
|
|
|