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Bayesian Statistics
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Bayesian Statistics

Master Bayesian statistics: Learn Bayes' rule, prior and posterior probabilities, credible intervals. Apply to real-world problems using R.

Course Cost

₹ 2,699

Intermediate

Skill Level

33 Hours

Self-paced lessons

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.

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3.8

74,921 Enrolled

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Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel

David Banks

David Banks

English

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olive-leaves-logo

3.8

74,921 Enrolled

olive-leaves-logo

English

Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel

David Banks

David Banks

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

  • Use R to implement Bayesian analyses and interpret results

  • Apply Bayesian methods to real-world problems in various fields

Skills you'll gain

bayesian statistics
bayes rule
prior probability
posterior probability
credible intervals
bayesian inference
bayesian regression
R programming

This course includes:

3.88 Hours PreRecorded video

12 quizzes,1 peer review

Access on Mobile, Tablet, Desktop

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

Financial Aid

Reviews

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Mine Çetinkaya-Rundel
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4.9

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138

Reviews

4,01,380

Students

9

Courses

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.
David Banks
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3.6

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52

Reviews

75,966

Students

2

Courses

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.
Merlise A Clyde
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3.6

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54

Reviews

76,302

Students

2

Courses

Dr. Merlise A. Clyde is a Professor of Statistical Science at Duke University, where she has served as Chair of the Department since 2013. She earned her Ph.D. in 1993 from the University of Minnesota and joined Duke's faculty in the fall of the same year, initially at the Institute of Statistics and Decision Sciences, which is now known as the Department of Statistical Science. Dr. Clyde is a past President of the International Society of Bayesian Analysis (ISBA) and is an elected Fellow of both ISBA and the American Statistical Association. Her research primarily focuses on Bayesian methodologies for feature and model selection, as well as prediction, utilizing Bayesian Model Averaging to address model uncertainty with an emphasis on prior choice and computational techniques. Dr. Clyde has developed several R packages to support her research, including BAS (Bayesian Adaptive Sampling for Bayesian Model Averaging). Her work is inspired by applications across various fields, including environmental health, chemistry, genomics, neuroscience, and astronomy.
Colin Rundel
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3.6

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52

Reviews

75,966

Students

2

Courses

Colin Rundel is an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. He earned his Ph.D. in Statistics from the University of California, Los Angeles, and holds a B.S. in Biology from the California Institute of Technology. Dr. Rundel's research focuses on spatial and computational statistics, particularly in relation to applied biological and ecological issues. His recent projects have involved modeling the migratory routes of North American songbirds, developing large-scale fusion models for air pollution, and creating models to detect landmines and IEDs through the integration of multiple sensors. Additionally, he is dedicated to enhancing computational pedagogy within the statistics curriculum, with a specific emphasis on training statisticians to create high-quality, reproducible software as an integral part of their analysis workflow.
Bayesian Statistics

Intermediate

Skill Level

33 Hours

Self-paced lessons

Course Cost

₹ 2,699

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.