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Bayesian Regression Modeling with rstanarm
4.8(69)7k studentsADVANCEDEnglish
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Course Overview
This advanced course focuses on estimating linear regression models within a Bayesian framework using R and the rstanarm package, offering an alternative to p-value-dependent modeling.
Who Should Take This Course
Tailored for advanced statisticians, R programmers, and data analysts looking to build robust Bayesian regression models and evaluate predictive distributions.
Key Takeaways
- Formulate and fit Bayesian linear regression models using the rstanarm R package.
- Understand prior distributions and conduct posterior predictive checks.
- Compare Bayesian models and generate predictions for unseen datasets.
About this course
Bayesian estimation offers a flexible alternative to modeling techniques where the inferences depend on p-values. In this course, you’ll learn how to estimate linear regression models using Bayesian methods and the rstanarm package. You’ll be introduced to prior distributions, posterior predictive model checking, and model comparisons within the Bayesian framework. You’ll also learn how to use your estimated model to make predictions for new data.
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