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Bayesian Regression Modeling with rstanarm

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
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4 Hours15 Videos45 Exercises4,519 Learners
3400 XP

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Course Description

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.

  1. 1

    Introduction to Bayesian Linear Models

    Free
    A review of frequentist regression using lm(), an introduction to Bayesian regression using stan_glm(), and a comparison of the respective outputs.
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  2. 2

    Modifying a Bayesian Model

    Learn how to modify your Bayesian model including changing the number and length of chains, changing prior distributions, and adding predictors.
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  3. 3

    Assessing Model Fit

    In this chapter, we'll learn how to determine if our estimated model fits our data and how to compare competing models.
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  4. 4

    Presenting and Using a Bayesian Regression

    In this chapter, we'll learn how to use the estimated model to create visualizations of your model and make predictions for new data.
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In the following tracks
Machine Learning ScientistStatistician
Collaborators
David CamposChester IsmayShon Inouye
Jake Thompson Headshot

Jake Thompson

Psychometrician, ATLAS, University of Kansas
Jake is a Psychometrician at the Center for Accessible Teaching, Learning, and Assessment Systems (ATLAS) and received his PhD in Educational Psychology and Research. His interests are include educational assessment, diagnostic classification modeling, and Bayesian inference. Follow him at @wjakethompson on Twitter or on his blog.
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