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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 delivered as private, customized, on-site training at our clients' locations worldwide for groups of 3 or more attendees and are custom tailored to their specific needs. Please visit our client list to see organizations for whom we have delivered private in-house training. These courses can also be delivered as live, private online classes for groups that are geographically dispersed or wish to save on the instructor's or students' travel expenses. To receive a customized proposal and price quote for private training at your site or online, 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.

Contact Us

Toll-free in US/Canada:
877 849 1850
International:
+1 678 648 3113

Toll-free in US/Canada:
866 566 1228
International:
+1 404 420 2491

925B Peachtree Street, NE
PMB 378
Atlanta, GA 30309-3918
USA

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