This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
- 5 stars45.15%
- 4 stars20.66%
- 3 stars14.54%
- 2 stars9.18%
- 1 star10.45%
Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.
The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.
I wanted to tools for Bayesian Statistics to be as functional as the other tools available. No problem with the class. I think the material will get there for R.
I find the teaching a bit unclear. I still don't sure I understand how to use Bayesianinference on problems I encounter in my work.
What background knowledge is necessary?
Will I receive a transcript from Duke University for completing this course?