Bayesian Modelling with Regression ( From A to Z ) with R
What you'll learn
- Bayesian Predictive Modelling with Regression using R statistical software , The content includes both Probabilistic approach and non_probabilistic one
- There is no prerequisite for the course , except some brief familiarity with the Bayesian thinking
“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 ...
Who this course is for:
- Anyone who is interested to know how to start a project applying Bayesian and finish it in Bayesian
I am graduate student in Applied Mathematics at York university , Toronto , Ontario , Canada. Previously , I had done another graduate degree in Theoretical Particle Physics which after that I joined a research team at the Montreal Neurological Institute and did research on Alzheimer`s disease and application of the AI in the diagnosis of the disorder before the initial symptoms of the dementia from the PET scans.
Currently , I do both conduct a research on the application of the AI in the early diagnosis of cancer from the scattering coefficients of the lasers from the cancerous tissues.
and also , I do teach mathematics and Physics at a college which recently I won the Canadian Meritorious Award for advising a team to win the first place at the 5th Annual Mathematical Modelling Challenge 2020.
I do like reading philosophical text books , then when I am not busy with research or teaching , I do spend my time reading books in a coffee shop.