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Bayesian Regression Modeling with rstanarm

AdvancedSkill Level

4.8+

Updated 12/2021

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

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.

Prerequisites

Bayesian Modeling with RJAGS Introduction to Data Visualization with ggplot2 Intermediate Regression in R

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.

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

Start Chapter

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.

What's in a Bayesian Model?

50 XP

Altering chains

100 XP

Do I have enough iterations?

50 XP

Prior distributions

50 XP

Determine Prior Distributions

100 XP

Calculate Adjusted Scales

100 XP

Unadjusted Priors

100 XP

User Specified Priors

50 XP

Changing Priors

100 XP

Specifying informative priors

100 XP

Consequences of informative priors

50 XP

Altering the estimation process

50 XP

Altering the Estimation

100 XP

Start Chapter

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.

Using the R Squared statistic

50 XP

Calculating Frequentist R-squared

100 XP

R-squared for a Bayesian Model

100 XP

Posterior predictive model checks

50 XP

Predicted score distributions

100 XP

Distributions for a single observation

50 XP

Model fit with posterior predictive model checks

50 XP

R-squared Posterior

100 XP

Posterior Predictive Testing

100 XP

Bayesian model comparisons

50 XP

Calculating the LOO estimate

100 XP

Comparing models

50 XP

Start Chapter

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.

Visualizing a Bayesian model

50 XP

Plotting a Bayesian model

100 XP

Plotting Model Uncertainty

100 XP

Making predictions

50 XP

Popularity for Observed Songs

100 XP

Popularity for New Songs

100 XP

Visualizing predictions

50 XP

Format prediction posteriors

100 XP

Visualize New Predictions

100 XP

Conclusion

50 XP

Start Chapter

Bayesian Regression Modeling with rstanarm

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FAQs

Is this course suitable for beginners?

No. This coursed is aimed at Advanced learners with strong experience in programming in R.

Will I receive a certificate at the end of the course?

Yes, upon completing the course, you will receive a certificate of completion.

What topics does this course cover?

This course covers a variety of topics related to Bayesian regression and the rstanarm package. This includes reviewing frequentist regression and establishing core principles in the Bayesian framework, modifying a Bayesian model, assessing model fit, and presenting and using a Bayesian regression.

Who will benefit from this course?

This course is useful for anyone interested in developing a deeper understanding of Bayesian regression, especially data scientists, statisticians, analysts, and software developers.

What technical requirements should I have before taking this course?

For this course, you must be familiar with linear regression, basic probability, and the R programming language. Additionally, a working knowledge of the ggplot2 and tidyverse packages is beneficial but not required.

How much time should I expect to spend on this course?

This course should take approximately 4 hours to complete

Can I use a different software package for the exercises in this course?

This course focuses on the rstanarm package, so it is recommended that you use this package for the exercises. However, the concepts and techniques learned in this course should be applicable to other software packages as well.

How does Bayesian modeling differ from frequentist modeling?

Bayesian modeling is an alternative to frequentist modeling, which is heavily focused on p-values and hypothesis testing. With Bayesian modeling, uncertainty is expressed in the form of a probability distribution and parameters are expressed in terms of the expected values of their posterior distributions.

Bayesian Regression Modeling with rstanarm

Training a Team?

Course Description

Prerequisites

Introduction to Bayesian Linear Models

Modifying a Bayesian Model

Assessing Model Fit

Presenting and Using a Bayesian Regression

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Is this course suitable for beginners?

Will I receive a certificate at the end of the course?

What topics does this course cover?

Who will benefit from this course?

What technical requirements should I have before taking this course?

How much time should I expect to spend on this course?

Can I use a different software package for the exercises in this course?

How does Bayesian modeling differ from frequentist modeling?

Join over 19 million learners and start Bayesian Regression Modeling with rstanarm today!

Grow your data skills with DataCamp for Mobile

Course Description

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Will I receive a certificate at the end of the course?

What topics does this course cover?

Who will benefit from this course?

What technical requirements should I have before taking this course?

How much time should I expect to spend on this course?

Can I use a different software package for the exercises in this course?

How does Bayesian modeling differ from frequentist modeling?

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 million learners and start Bayesian Regression Modeling with rstanarm today!

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Join over 19 million learners and start Bayesian Regression Modeling with rstanarm today!