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 stars45.03%
- 4 stars20.61%
- 3 stars14.63%
- 2 stars9.16%
- 1 star10.55%
TOP REVIEWS FROM BAYESIAN STATISTICS
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
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.
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!
Great course. Quite difficult though. I wished it was split to two course or maybe an entire specialization dedicated for this.
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