This course is part of Bayesian Statistics Using R.
This advanced course builds upon fundamental Bayesian inference concepts, diving deep into sophisticated statistical methods and algorithms. Students explore mixed effects regression models crucial for analyzing hierarchical data in biostatistics, ecology, and health sciences. The curriculum covers advanced topics including Markov Chain Monte Carlo (MCMC) implementation, convergence assessment, Bayesian model averaging, and handling missing data. Through practical examples, participants learn to apply these advanced techniques using R programming.
Instructors:
English
English
What you'll learn
Master implementation of MCMC algorithms in R programming
Apply mixed effects regression models for complex hierarchical data
Understand and utilize Bayesian model averaging techniques
Develop skills in handling various types of missing data
Implement advanced statistical methods for real-world applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by

Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.





Module Description
The course provides comprehensive coverage of advanced Bayesian statistical methods using R. Students learn to implement mixed effects regression models, understand and create MCMC algorithms, apply Bayesian model averaging, and handle missing data scenarios. The curriculum emphasizes practical applications in fields like biostatistics, ecology, and health sciences, with hands-on implementation in R.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Bayesian Statistics Using R
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.
Testimonials
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.
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.