Bayesian Modelling with Regression ( From A to Z ) with R
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Brief Introduction
Start and Finish your project in BayesianDescription
“No thief, however skillful, can rob one of knowledge, and that is why knowledge is the best and safest treasure to acquire.”
― L. Frank Baum, The Lost Princess of Oz
When I was doing my graduate studies in Applied Mathematics , I was overwhelmed with the number of the books in Bayesian with many theories and wonderful mathematical equations , but I was completely paralyzed when I started my first project trying to apply Bayesian methods.
I did not know where should I start and how to interpret any parameters which I made an inference about , there was not enough sources to walk me through from A to Z.
I hope this lectures fills in that gap and acts as a bridge that help you as student , researcher or practitioner who wants to apply Bayesian methods in regression in order to successfully make the probabilistic inference.
At each step , I would run the same model both in Bayesian and non-Bayesian framework , in order to enable you to see the difference between two different approaches and see how you need to interpret the difference.
Also , for those who are interested in predictive modelling , I have included lectures on real data for model comparison , model selection , cross validation and ultimately methods to visualize the uncertainty in your modelling.
However , before we start in complete Bayesian , I devoted one lecture to remind you of what we have seen in monotone and additive models in Non_Bayesian and , I look at it as a warm up before we start the course together.
I`d like to thank you for joining me for this wonderful journey and I hope we an all form a community starting from here , in order to share our insights , questions and continue to work together to lean more about Bayesian .
Let`s begin the journey ...
Requirements
- Requirements
- There is no prerequisite for the course , except some brief familiarity with the Bayesian thinking
Bayesian Modelling with Regression ( From A to Z ) with R
- 0.0
-
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Start and Finish your project in Bayesian “No thief, however skillful, can rob one of knowledge, and that is why knowledge is the best and safest treasure to acquire....
