Introduction to Bayesian Inference with R


Course Number: RPROG-108

Duration: 3 days (19.5 hours)

Format: Live, hands-on

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, online classes for individuals. See a schedule of online courses.

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

Introduction to Software Environment (R and RStudio)
Review of Base R
  • Data import
  • Creating new variables
  • Basic summaries
  • Plotting with R
Probability Theory and Notation with Applied Examples
Bayesian Models Versus Traditional Models
  • The difference between a frequentist approach and a Bayesian approach
  • Estimating cluster offsets
  • Shrinkage
Estimating a Single Parameter
  • Combing the prior and observed data
  • The notion of a non-informative prior
  • Summarizing the posterior
  • Implementing MCMC algorithms
  • Diagnosing MCMC chain output
  • Checking posterior output
Applied Bayesian Regression Modelling: Normal Linear Regression
  • Contrasting the Bayesian approach to linear regression
  • Establishing model and data matrices
  • Dimensionality reduction in the context of linear modeling
  • Penalized models (shrinkage)
  • Appropriate priors for beta and covariance parameters
  • Diagnosing MCMC chain output
  • Checking posterior output
  • Non-linear terms
  • Seasonal terms
  • Extending this framework to clustered data
  • Extensions to repeated measurements
Applied Bayesian Regression Modelling: Logistic Regression
  • Extending Bayesian models to binary outcomes
  • Accounting for over and under dispersion in a binomial model
  • Extensions to clustered data
  • Extensions to repeated measurements
Applied Bayesian Regression Modelling: Time to Event Models
  • Extending Bayesian approaches to proportional hazards modeling
Review of Other Software Approaches to Performing Bayesian Inference
  • INLA
  • WINBUGS/OPENBUGS
  • JAGS
  • STAN
Conclusion

Training Materials:

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

Software Requirements:

  • R 3.5 or later with console
  • IDE or text editor of your choice (RStudio recommended)


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