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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 Exercises5,236 Learners3400 XPMachine Learning Scientist TrackStatistician Track

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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


    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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    Non-Bayesian Linear Regression
    50 xp
    Exploring the data
    100 xp
    Fitting a frequentist linear regression
    100 xp
    Bayesian Linear Regression
    50 xp
    Fitting a Bayesian linear regression
    100 xp
    Convergence criteria
    50 xp
    Assessing model convergence
    50 xp
    Comparing frequentist and Bayesian methods
    50 xp
    Difference between frequentists and Bayesians
    50 xp
    Creating credible intervals
    100 xp

In the following tracks

Machine Learning ScientistStatistician


dcamposlizDavid CamposchesterChester IsmayshoninouyeShon 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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