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Curs

Bayesian Regression Modeling with rstanarm

AvansatNivel de competențe

Actualizat 12.2021

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

Începe cursul gratuit

RProbability & Statistics

4 h

15 videoclipuri

45 Exerciții

3,400 XP

7,081

Certificat de realizare

Îndrăgit de cursanți din mii de companii

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Descrierea cursului

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.

Cerințe prealabile

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

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.

Non-Bayesian Linear Regression

Exploring the data

Fitting a frequentist linear regression

Bayesian Linear Regression

Fitting a Bayesian linear regression

Convergence criteria

Assessing model convergence

Comparing frequentist and Bayesian methods

Difference between frequentists and Bayesians

Creating credible intervals

Începe capitolul

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.

What's in a Bayesian Model?

Altering chains

Do I have enough iterations?

Prior distributions

Determine Prior Distributions

Calculate Adjusted Scales

Unadjusted Priors

User Specified Priors

Changing Priors

Specifying informative priors

Consequences of informative priors

Altering the estimation process

Altering the Estimation

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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.

Using the R Squared statistic

Calculating Frequentist R-squared

R-squared for a Bayesian Model

Posterior predictive model checks

Predicted score distributions

Distributions for a single observation

Model fit with posterior predictive model checks

R-squared Posterior

Posterior Predictive Testing

Bayesian model comparisons

Calculating the LOO estimate

Comparing models

Începe capitolul

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.

Visualizing a Bayesian model

Plotting a Bayesian model

Plotting Model Uncertainty

Making predictions

Popularity for Observed Songs

Popularity for New Songs

Visualizing predictions

Format prediction posteriors

Visualize New Predictions

Începe capitolul

Bayesian Regression Modeling with rstanarm

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