I3 Bayesian parameter estimation with a binomial model example

Bayesian statistics uses Bayes' Rule to make decisions. In the context of a parametric model, parameters are updated from the prior using data to obtain the posterior using Bayes' Rule. From this posterior, Bayes' estimators and credible intervals can be obtained. The video discusses the posterior expectation and equal-tail credible intervals. As an example throughout, the video uses a binomial model with a uniform prior on the probability of success. The posterior is shown (but not derived) to be a beta distribution. A brief detour to discuss the beta distribution ensures. R code is provided to perform all of the Bayesian analyses for binomial data.
Probability playlist: • STAT 587 - Probability
Statistics playlist: • STAT 587 - Inference
STAT 587: www.jarad.me/courses/stat587Eng/
STAT 587 Videos: www.jarad.me/courses/stat587E...
Slides: www.jarad.me/courses/stat587E...
00:58 - Bayesian statistician
02:24 - Bayes' Rule
04:54 - Bayesian parameter estimation
07:10 - Bayesian notation
08:10 - Binomial model
10:53 - Beta distribution
12:52 - Beta densities
13:42 - Binomial example
15:09 - Posterior density
16:03 - Posterior expectation
17:44 - Credible intervals
19:37 - Credible interval visualization
20:05 - Summary
20:46 - Bayesian analysis for binomial model summary
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