For those who are interested in the fundamental mathematics that connects Bayesian, Likelihood, and Frequentist inference, Dr. Korbinian Strimmer from Manchester University has written and published these course notes I’m finding helpful.
Understanding the information here will aid in understanding the applications in RMS.
Strimmer, K (2021) Statistical Methods: Likelihood, Bayes and Regression Course Notes
The link in the original post is broken. Here is a current link:
I stumbled upon this old post by chance.
I’ve always been interested in Bayesian methods and have been collecting learning materials. However, these materials are all about statistical theory. Of course, theory is very important, but it’s a bit difficult for someone like me who is not a statistics major.
What I want to know is whether anyone can recommend some simple learning materials. It’s best if they come with real research cases.
For example, the research methods I often use now are the Cox models. For instance, if I want to know if blood pressure is related to the incidence of stroke in ten years, I can easily obtain the hazard ratio through the Cox method. We calculate standard errors and p-values and then look at confidence intervals. What I want to know is if there are Bayesian methods for dealing with such problems.
My understanding of statistics and Bayesian methods is very limited, so if I’m wrong, I would greatly appreciate some simple and understandable explanation.
Thank you.
Though not specific to Cox models there are many general Bayesian resources that are not mathematical here.