Introduction to Bayesian Inference with R

Bayesian Inference with R Training Overview

Accelebrate's Introduction to Bayesian Inference with R course teaches attendees the Bayesian approach to inference using the R language as the applied tool. After a quick review of importing and managing data with R as well as base R commands, students learn the theoretical underpinnings of inference (with a focus on Bayesian statistics), along with applied examples of Bayesian approaches to statistical models.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

Objectives

• Understand how to import data to R for use in statistical modeling
• Review the frequentist approach to making inference on populations, using samples of data
• Non-comprehensive review of probability theory
• Understand maximum likelihood and restricted maximum likelihood
• Contrast frequentist approaches to inference with Bayesian approaches to inference
• Understand how prior distributions affect posterior distributions
• Review the difference between proper and improper priors
• Understand how to implement and explain an MCMC algorithm for obtaining empirical prior distributions
• Fit Bayesian modeling approaches to the general linear modeling framework
• Account for clustering and repeated events over time using Bayesian inference (generalized linear models)
• Make inference on functions of parameters
• Properly interpret Bayesian posterior density intervals
• Develop awareness of different modern software approaches to making Bayesian inference (with a focus on R)

Prerequisites

Students should have a basic background in R programming including importing and manipulating data, and an understanding of base R data structures such as vectors, matrices, lists, and dataframes. Students should also have a basic background in frequentist statistics to include hypothesis testing (p-values and null hypotheses), and statistical tests such as t-tests and chi-square tests. An understanding of the general linear modeling framework will be helpful, but is not required for this course.

Outline

Expand All | Collapse All

Training Materials

All R training attendees receive comprehensive courseware covering all topics in the course.

Software Requirements

• A recent release of R 4.x
• IDE or text editor of your choice (RStudio recommended)