Bayesian Regression Modeling with rstanarm
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
- 4 hours
- 15 Videos
- 45 Exercises
- 734 Participants
- 3,400 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.
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Course Outline
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1
Introduction to Bayesian Linear Models
FreeA 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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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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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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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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1
Introduction to Bayesian Linear Models
FreeA review of frequentist regression using lm(), an introduction to Bayesian regression using stan_glm(), and a comparison of the respective outputs.
-
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.
-
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.
-
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.
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.
See MoreCollaborators
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Chester Ismay
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David Campos
-
Shon Inouye
Course Instructor
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.
See MoreCollaborator(s)
-
Chester Ismay
-
David Campos
-
Shon Inouye
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