About this course: 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. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
Taught by: Mine Çetinkaya-Rundel, Assistant Professor of the Practice
Department of Statistical Science
Taught by: David Banks, Professor of the Practice
Statistical Science
Taught by: Colin Rundel , Assistant Professor of the Practice
Statistical Science
Taught by: Merlise A Clyde, Professor
Department of Statistical Science
| Basic Info |
Course 4 of 5 in the
Statistics with R Specialization.
|
| Level | Intermediate |
| Commitment | 5 weeks of study, 5-7 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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| Earn a shareable Course Certificate | Not available | Available |
very intuitive!
The classes are good.
very difficult but it is interesting
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Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.