Bayesian Statistics in R

Bayesian Statistics in R

taught by Peter Congdon

 

 
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Aim of Course:

After taking this online course, "Bayesian Statistics in R" you will be able to install and run rjags, a program for Bayesian analysis within R.  Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.   Procedures covered from a Bayesian perspective include linear regression, Poisson, logit and negative binomial regression, and ordinal regression.

This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

Note:  You will interact with your colleagues and the instructor on a daily basis via a private discussion forum.

WEEK 1: Using rjags for Bayesian inference in R: Introductory Ideas and Programming Considerations

  • Basic Principles of Bayesian Inference and MCMC Sampling 
  • R and rjags for Bayesian inference. Initial values, posterior summaries, checking convergence.
  • JAGS and BUGS programming Syntax, with simple applications


WEEK 2: Linear Regression with rjags

  • Specifying Models
  • Specifying Priors on Regression Coefficients and Residual Variances
  • Posterior Summarisation in R


WEEK 3: Regression for Count, Binary and Binomial Data

  • Poisson Regression
  • Logit and Probit Regression
  • Negative Binomial Regression


WEEK 4:  Other Regression Techniques

  • Ordinal and multinomial regression
  • Categorical predictors
  • Predictor selection


HOMEWORK:

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using

software.

This course also has example software codes, supplemental readings available online, and an end of course project.

 

 

Bayesian Statistics in R

Who Should Take This Course:
You should take this course if you are familiar with R and with Bayesian statistics at the introductory level, and work with or interpret statistical models and need to incorporate Bayesian methods.  Analysts who need to incorporate their work into real-world decisions, as opposed to formal statistical inference for publication, will be especially interested.  This includes business analysts, environmental scientists, regulators, medical researchers, and engineers.   Note:  In this course you will learn both BUGS coding and how to integrate it into R.  If you are not familiar with BUGS, and want to take the time to learn BUGS first, consider taking the optional prerequisite listed below.
Prerequisite:

Organization of the Course:
Options for Credit and Recognition:

Specialization:
Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Bayesian Statistics Specialization which uses Bayes' Theorem to perform analyses and computations, and learn what makes it so popular.

Course Text:
The BUGS Book - A Practical Introduction to Bayesian Analysis, David Lunn et al. CRC Press (2012).  Note: This book is an excellent guide to BUGS; it is not specifically about R, but all required instruction about R coding will be provided in the course materials.  If you are already well familiar with BUGS and have your own reference, you may not need this book.
Software:

JAGS (Just Another Gibbs Sampler)

The R project

The course will focus on use of RJAGS. An rjags implementation in R rests crucially on coding in JAGS, which is virtually identical to BUGS.

Instructor(s):

Dates:

September 20, 2019 to October 18, 2019 March 20, 2020 to April 17, 2020

Bayesian Statistics in R

Instructor(s):

Dates:
September 20, 2019 to October 18, 2019 March 20, 2020 to April 17, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Click here to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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