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.
Bayesian StatisticsDuke University
About this Course
Skills you will gain
- 5 stars45.09%
- 4 stars20.63%
- 3 stars14.52%
- 2 stars9.17%
- 1 star10.57%
TOP REVIEWS FROM BAYESIAN STATISTICS
Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!
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.
It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.
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.
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