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 stars45.09%
- 4 stars20.63%
- 3 stars14.52%
- 2 stars9.17%
- 1 star10.57%
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.
This is the hardest courses I have taken. I hoped to have more supplemental reading materials and more practical exercises in R.
An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed
This course and the others that are part of the specialization are excellent. Those of us who are beginners in Bayesian Statistics may find the material a bit confusing.
What background knowledge is necessary?
Will I receive a transcript from Duke University for completing this course?