Bayesian Statistics
Master Bayesian statistics: Learn Bayes' rule, prior and posterior probabilities, credible intervals. Apply to real-world problems using R.
Master Bayesian statistics: Learn Bayes' rule, prior and posterior probabilities, credible intervals. Apply to real-world problems using R.
This intermediate-level course introduces Bayesian statistics, focusing on updating inferences as evidence accumulates. Students learn to use Bayes' rule, transform prior probabilities into posterior probabilities, and understand the Bayesian paradigm. The course covers practical applications, including Bayesian comparisons of means and proportions, Bayesian regression, and inference using multiple models. Implementation in R is emphasized, providing end-to-end Bayesian analyses from framing questions to building models and eliciting prior probabilities.
3.8
(791 ratings)
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What you'll learn
Use Bayes' rule to transform prior probabilities into posterior probabilities
Understand and apply the concepts of prior, likelihood, and posterior probability
Implement Bayesian inference for various statistical problems
Conduct Bayesian hypothesis testing and model comparison using Bayes Factors
Perform Bayesian linear regression and model averaging
Make optimal decisions based on Bayesian statistics
Skills you'll gain
This course includes:
3.88 Hours PreRecorded video
12 quizzes,1 peer review
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FullTime access
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There are 7 modules in this course
This course provides a comprehensive introduction to Bayesian statistics, covering fundamental concepts and practical applications. Students will learn to use Bayes' rule, understand prior and posterior probabilities, and apply Bayesian methods to real-world problems. The curriculum includes Bayesian inference, decision making, hypothesis testing, and regression. Practical implementation using R is emphasized throughout the course. By the end, learners will be able to conduct end-to-end Bayesian analyses, from framing questions to building models and interpreting results.
About the Specialization and the Course
Module 1 · 1 Hours to complete
The Basics of Bayesian Statistics
Module 2 · 6 Hours to complete
Bayesian Inference
Module 3 · 7 Hours to complete
Decision Making
Module 4 · 7 Hours to complete
Bayesian Regression
Module 5 · 7 Hours to complete
Perspectives on Bayesian Applications
Module 6 · 1 Hours to complete
Data Analysis Project
Module 7 · 4 Hours to complete
Fee Structure
Payment options
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Instructors
Associate Professor of the Practice at Duke University
Dr. Mine Çetinkaya-Rundel is an Associate Professor of the Practice in the Department of Statistical Science at Duke University. She earned her Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Çetinkaya-Rundel is dedicated to innovative statistics pedagogy, focusing on developing student-centered learning tools for introductory statistics courses. Her recent work emphasizes teaching computation at the introductory level with a strong commitment to reproducibility and addressing the gender gap in self-efficacy within STEM fields. Additionally, her research interests include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and actively contributes to the OpenIntro project, which aims to create open-licensed educational materials that reduce barriers to education. Dr. Çetinkaya-Rundel also co-edits the Citizen Statistician blog and contributes to the "Taking a Chance in the Classroom" column in Chance Magazine.
Professor of the Practice
David Banks is a Professor of the Practice in the Department of Statistical Science at Duke University, where he earned his PhD in statistics in 1984 from Virginia Tech. His academic career includes positions at Carnegie Mellon University and the University of Cambridge, as well as six years in various federal government roles at NIST, the DOT, and the FDA. His research focuses on evidence-based public policy, adversarial risk analysis, dynamic text networks, statistical computation, and metabolomics. Banks has served as an editor for the Journal of the American Statistical Association and was the founding editor of Statistics and Public Policy. He has led data mining research initiatives at the Isaac Newton Institute and the Statistical and Applied Mathematical Sciences Institute, served two terms on the Board of Directors of the American Statistical Association, and currently holds a position on the board of the International Society for Bayesian Analysis. Additionally, he is the president of the International Society for Business and Industrial Statistics and a past president of the Classification Society.
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3.8 course rating
791 ratings
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