Bayesian Regression Modeling with rstanarm

0
Language

Level

Beginner

Access

Paid

Certificate

Paid

Category:

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

Add your review

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.

What You’ll Learn

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.

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.

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.

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.

User Reviews

0.0 out of 5
0
0
0
0
0
Write a review

There are no reviews yet.

Be the first to review “Bayesian Regression Modeling with rstanarm”

×

    Your Email (required)

    Report this page
    Bayesian Regression Modeling with rstanarm
    Bayesian Regression Modeling with rstanarm
    LiveTalent.org
    Logo
    LiveTalent.org
    Privacy Overview

    This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.