Bayesian Computational Analyses with R
Bayesian Computational Analyses with R is an introductory course that teaches you how to use and implement Bayesian modeling in R. The Bayesian approach is an alternative to the "frequentist" approach, which involves taking a sample of data and inferring about the population's likely parameters. The Bayesian approach, on the other hand, estimates the most likely values and distributions for the estimated population parameters (the 'posterior') using both likelihood functions and a sample of observed data (the 'prior').
The course is appropriate for both novice and intermediate Bayesian students as well as Bayesian practitioners who want to learn about Bayesian concepts. It is both a practical, "hands-on" course with many examples using R scripts and software, as well as a conceptual course in which the Bayesian concepts are explained. The course materials include all materials, software, R scripts, slides, exercises, and solutions. It's beneficial to have a basic understanding of inferential statistics and probability theory. Experience with R is not required, but it is advantageous.
Who this course is for:
- The course is ideal for anyone interested in learning both the conceptual and practical side of using Bayes' Rule to model likely event outcomes.
- The course is best suited for both students and professionals who currently make use of quantitative or probabilistic modeling.
- It is useful to have a working knowledge of either basic inferential statistics or probability theory.
- It is NOT necessary to have prior experience using R software to successfully complete and to benefit from this course.
Requirements
- Students will need to install R and RStudio software, but ample instruction for doing so is provided in the course materials.
Course ratings: 4.2/5
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