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Introduction to Bayesian Inference with R

Ascendient Learning'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...

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Duration 3 days
Course Code RPROG-108
Available Formats Classroom

Overview

Ascendient Learning'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.

Skills Gained

  • 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.

Course Details

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)

Outline

  • 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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Schedule

Upcoming Course Dates

There are currently no scheduled dates for this course. If you are interested in this course, contact us with the form below.

Contact Information

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

ExitCertified was a great. They gave me all the materials and information I needed ahead of time to prepare for the course.

Simply great training provider that I can go for updating/acquiring my skill sets.

This was effective way to provide a ton of information in a short time period.

The class covered the concepts needed for the AWS Cloud Practitioner Certification.

Brandon was a great instructor. The virtual course materials and labs provided were very informative.