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
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
- 5 stars45.19%
- 4 stars20.48%
- 3 stars14.59%
- 2 stars9.21%
- 1 star10.49%
TOP REVIEWS FROM BAYESIAN STATISTICS
Very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking and a good math/analytic background is helpful.
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.
This is the hardest courses I have taken. I hoped to have more supplemental reading materials and more practical exercises in R.
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.
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