Master practical Bayesian data analysis using R programming through this comprehensive course covering fundamental concepts and applications.
Master practical Bayesian data analysis using R programming through this comprehensive course covering fundamental concepts and applications.
This introductory course provides a practical foundation in Bayesian statistical analysis using R programming. Students learn to apply Bayesian approaches to real-world data analysis across various fields including epidemiology, economics, and political sciences. The curriculum covers essential concepts such as Bayes' Theorem, posterior inference, conjugate models, and Markov Chain Monte Carlo methods. Through hands-on exercises, participants develop skills in Bayesian regression analysis, cluster analysis, and model diagnostics using R software.
3.8
(9 ratings)
Instructors:
English
English
What you'll learn
Apply Bayes' Theorem to real-world data analysis problems
Implement posterior inference and credible interval calculations
Use conjugate models for statistical analysis
Perform Bayesian regression and ANOVA
Master Markov Chain Monte Carlo methods in R
Conduct Bayesian cluster analysis
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course introduces students to practical Bayesian data analysis using R programming. The curriculum covers fundamental concepts including Bayes' Theorem, posterior inference, conjugate models, and Markov Chain Monte Carlo methods. Students learn to apply Bayesian approaches to real-world problems through regression analysis, cluster analysis, and model diagnostics. The course emphasizes practical implementation using R software and includes applications in various fields such as epidemiology, economics, and political sciences.
Fee Structure
Instructor

4 Courses
A Distinguished Leader in Statistical Modeling and Epidemiology
Elena Moltchanova serves as Professor of Statistics and Head of the Statistical Consulting Unit at the University of Canterbury, where she has established herself as a leading expert in applied Bayesian statistics and spatial modeling. Her academic journey began at the University of Helsinki, Finland, where she completed her MSc in Statistics, followed by a PhD from the University of Jyvaskyla focusing on applications of spatial statistics in epidemiology. Her career includes significant contributions at IIASA's Ecosystems Services and Management Program, where she first joined as a Young Scientists Summer Program participant in 2001, earning the Michailevich Scholarship for her work on image restoration. Since joining the University of Canterbury in 2011, she has published extensively, with over 50 papers spanning epidemiology of chronic diseases, extreme event modeling, and forest ecology. Her research impact is evidenced by over 9,300 citations and an h-index of 40. Her expertise spans multiple statistical software platforms and programming languages, which she applies to diverse fields including climate modeling, rare extreme events analysis, and epidemiological studies. As Head of the Statistical Consulting Unit, she continues to bridge theoretical statistics with practical applications while maintaining active research collaborations across international institutions.
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