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LISA Statistics Short Course: Bayesian Methods for Regression in R
(Academic)
LISA (Laboratory for Interdisciplinary Statistical Analysis) is providing a series of evening short courses to help graduate students use statistics in their research. The focus of these two-hour courses is on teaching practical statistical techniques for analyzing or collecting data. See www.lisa.stat.vt.edu/?q=short_courses for instructions on how to REGISTER and to learn more.
Spring 2012 Schedule:
January 31: Designing Experiments and Collecting Useful Data;
February 7: How to Create a Successful Survey;
February 14: Statistical Analysis in R, Part I;*
February 16: Statistical Analysis in R, Part II;*
February 21 & February 23: Combining the Power of R and Excel: RExcel;*
February 28: Regression Analysis using JMP;
March 13: Bayesian Methods for Regression in R;*
*These courses will be held in 3060 Torgersen, all other courses will be held in GLC Room F.
Tuesday, March 13;
Instructor: Nels Johnson;
Title: Bayesian Methods for Regression in R;
An outline for questions I hope to answer:
What is Bayes Rule? (lecture portion)
> What is the likelihood?
> What is the prior distribution?
=> How should I choose it?
==> Why use a conjugate prior?
==> What is an informative versus uninformative prior?
> What is the posterior distribution?
=> How do I use it to make statistical inference?
=> How is this inference different from frequentist/classical inference?
=> What computational tools do I need in order to make inference?
How can I use R to do regression in a Bayesian paradigm? (computer portion)
> What libraries in R support Bayesian analysis?
> How do I use some of these libraries?
==> How do I interpret the output?
==> How do I produce diagnostic plots?
> What common topics do these libraries not support?
==> How can I do them myself?
==> How can LISA help me?
> What resources are available to help me Bayesian methods in R?
The main focus of this short course will be the Bayesian aspect of it. That means this is a slightly more advanced course requiring some knowledge of basic probability, regression methods, and the R software language.
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