2014年12月14日 · Bayesian Analysis, 1(1):1-40. there are 2 answers: Your model is first Bayesian if it uses Bayes' rule (that's the "algorithm"). More broadly, if you infer (hidden) causes from a generative model of your system, then you are Bayesian (that's the "function").

2015年8月14日 · 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.

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!

2013年7月30日 · 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 ...

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 ...

However, good Bayesian analyses consider the sensitivity of their results to the choice of prior distribution. That would seem to place us right back into the domain of power analysis. Although it might not go by that name and would be computed differently, the purpose would be the same: namely, determining how large a sample to obtain in order ...

Bayesian Estimation Supersedes the t-Test for John K. Kruschke is one of the most important papers that I had read explaining how to run the Bayesian analysis and how to make the plots. But the most important point is how to report the results from the plots to write up a paper?

See also this reference for a short but imho good overview of Bayesian reasoning and simple analysis. A longer introduction for conjugate analyses, especially for binomial data can be found here. A general introduction into Bayesian thinking can be found here. More slides concerning aspects of Baysian statistics are here.

2020年11月11日 · What is the proper Bayesian learning rate to use in this case? Alternatively, is my approach totally off and should I not be using a beta distribution for the prior in this situation? bayesian

2016年3月24日 · If we use Bayesian estimation/testing instead, having some prior (around 0?) for all comparisons, then yes the prior will shrink the posteriors toward zero, but we would still have randomly varying posteriors and/or Bayes factors and will probably have some cases out of 1000 that will look like "substantial" effects, even when true effects are ...