Bayesian Regression Modeling with rstanarm
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Mastering Bayesian Inference
If you are looking to move beyond traditional p-value-based modeling, this advanced course provides a deep dive into the Bayesian framework. By leveraging the rstanarm package, you will learn how to implement flexible estimation techniques that offer a more nuanced view of statistical uncertainty.
Key Learning Areas
- Model Estimation: Mastering linear regression through Bayesian methods.
- Distributional Theory: Understanding the role of prior distributions in your models.
- Validation & Comparison: Utilizing posterior predictive model checking and Bayesian model comparisons.
- Predictive Power: Applying your estimated models to generate predictions for new datasets.
This curriculum is ideal for advanced practitioners seeking to integrate robust probabilistic modeling into their data science workflows.
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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