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
- Bayesian Statistics
- Bayesian Linear Regression
- Bayesian Inference
- R Programming
Syllabus - What you will learn from this course
About the Specialization and the Course
The Basics of Bayesian Statistics
- 5 stars45.05%
- 4 stars20.55%
- 3 stars14.72%
- 2 stars9.13%
- 1 star10.53%
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!
This is the hardest courses I have taken. I hoped to have more supplemental reading materials and more practical exercises in R.
The course has seen a lot of improvement with new study materials and videos. I'd say that this is now much better than what the course was previously.
The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.
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