Bayesian Modelling with Regression ( From A to Z ) with R

Bayesian Modelling with Regression ( From A to Z ) with R

“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 ...

Start and Finish your project in Bayesian

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What you will learn

• Bayesian Predictive Modelling with Regression using R statistical software , The content includes both Probabilistic approach and non_probabilistic one

Rating: 3.7

Level: All Levels

Duration: 8.5 hours

Instructor: Omid Rezania