课程

Bayesian Regression Modeling with rstanarm

高级技能水平

更新时间 2021年12月

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

免费开始课程

RProbability & Statistics4 小时15 视频45 练习3,400 经验值7,038成就声明

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

深受数千家公司学习者的喜爱

培训2人或更多？

试用DataCamp for Business

课程描述

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.

先决条件

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

50 经验值

Exploring the data

100 经验值

Fitting a frequentist linear regression

100 经验值

Bayesian Linear Regression

50 经验值

Fitting a Bayesian linear regression

100 经验值

Convergence criteria

50 经验值

Assessing model convergence

50 经验值

Comparing frequentist and Bayesian methods

50 经验值

Difference between frequentists and Bayesians

50 经验值

Creating credible intervals

100 经验值

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?

50 经验值

Altering chains

100 经验值

Do I have enough iterations?

50 经验值

Prior distributions

50 经验值

Determine Prior Distributions

100 经验值

Calculate Adjusted Scales

100 经验值

Unadjusted Priors

100 经验值

User Specified Priors

50 经验值

Changing Priors

100 经验值

Specifying informative priors

100 经验值

Consequences of informative priors

50 经验值

Altering the estimation process

50 经验值

Altering the Estimation

100 经验值

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

50 经验值

Calculating Frequentist R-squared

100 经验值

R-squared for a Bayesian Model

100 经验值

Posterior predictive model checks

50 经验值

Predicted score distributions

100 经验值

Distributions for a single observation

50 经验值

Model fit with posterior predictive model checks

50 经验值

R-squared Posterior

100 经验值

Posterior Predictive Testing

100 经验值

Bayesian model comparisons

50 经验值

Calculating the LOO estimate

100 经验值

Comparing models

50 经验值

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

50 经验值

Plotting a Bayesian model

100 经验值

Plotting Model Uncertainty

100 经验值

Making predictions

50 经验值

Popularity for Observed Songs

100 经验值

Popularity for New Songs

100 经验值

Visualizing predictions

50 经验值

Format prediction posteriors

100 经验值

Visualize New Predictions

100 经验值

50 经验值

Bayesian Regression Modeling with rstanarm

课程完成

获得成就证明

将此证书添加到你的 LinkedIn 档案、简历或履历中
在社交媒体和绩效评估中分享立即注册

加入超过19百万学习者，今天就开始Bayesian Regression Modeling with rstanarm！

创建您的免费帐户

或

继续操作即表示您接受我们的《使用条款》和《隐私政策》，并同意您的数据存储在美国。

通过 DataCamp for Mobile 提升您的数据技能

随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。