Introduction to Bayesian Inference with R

RPROG-108 (3 Days)

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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

Most Accelebrate courses are taught as private, customized training for 3 or more attendees at our clients' sites worldwide. In addition, we offer live, private online classes for teams who may be in multiple locations or wish to save on travel costs. Please visit our client list for organizations for whom we have delivered onsite training. To receive a customized proposal and price quote for private on-site or online training, please contact us.

Bayesian Inference with R Training Objectives

All students will:

  • 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)

Bayesian Inference with R Training Outline

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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
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Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

RPROG-108

Duration:

3 Days

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.

Training Materials:

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

Software Requirements:

  • R 3.0 or later with console
  • IDE or text editor of your choice (RStudio recommended)
  • For classes delivered online, all participants need either dual monitors or a separate device logged into the online session so that they can do their work on one screen and watch the instructor on the other. A separate computer connected to a projector or large screen TV would be another way for students to see the instructor's screen simultaneously with working on their own.

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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