Introduction to Bayesian Computing and Techniques

Introduction to Bayesian Computing and Techniques

taught by Peter Congdon

 
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Aim of Course:

In this online course, "Introduction to Bayesian Computing and Techniques" students will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling the BUGS package (WinBUGS/OPENBUGS).

 

Participants will learn how to use BUGS software, use it to estimate parameters of standard distributions, and implement simple regression models.

 

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

WEEK 1: Introduction to Bayesian MCMC

  • Basic ideas of MCMC
  • Benefits of Bayes methods
  • Priors and Prior Informativeness
  • Important distributions in Bayesian analysis
  • Introduction to three standard schemes: (normal data, normal prior; binomial data, beta prior; poisson data, gamma prior)


WEEK 2: Bayesian Programming in BUGS

  • BUGS syntax and programs, data inputs, convergence checks, obtaining summaries


WEEK 3: Bayesian Posteriors

  • Main elements of posterior summarization
  • Tests on parameters or parameter collections (posterior probability tests)
  • Model predictions


WEEK 4: Bayesian Regression

  • Commonly-used regression models (normal, binary & binomial, poisson)

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

In addition to assigned readings, this course also has supplemental readings available online.

Introduction to Bayesian Computing and Techniques

Who Should Take This Course:
Statistical analysts and consultants who need to make decisions (or advise decision-makers) via a process that incorporates domain-specific information -- not simply abstract and arbitrary statistical rules.
Level:
intermediate
Organization of the Course:

this course has supplemental readings that are available online

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:

Extensive course notes are provided, and, particularly for those focusing on the implementation of techniques in Winbugs, the course may be followed without purchasing a text.

For a more in-depth look at the underlying concepts, and for those who require a book for reference, we recommend A First Course in Bayesian Statistical Methods, by Peter Hoff.

If you already have Bayesian Modeling Using WinBUGS, by I. Ntzoufras (2008, Wiley), that book is also a useful companion to this course, especially for Lesson 2.

Software:
The course will be based on the freeware BUGS package (WinBUGS/OPENBUGS).
Instructor(s):

Dates:

August 23, 2019 to September 20, 2019 February 21, 2020 to March 20, 2020

Introduction to Bayesian Computing and Techniques

Instructor(s):

Dates:
August 23, 2019 to September 20, 2019 February 21, 2020 to March 20, 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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The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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