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%
The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.
Great course. Quite difficult though. I wished it was split to two course or maybe an entire specialization dedicated for this.
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.
This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.
What background knowledge is necessary?
Will I receive a transcript from Duke University for completing this course?