Bayesian Methods for Regression in R
Course Topics
[video:https://vimeo.com/39411439]
LISA Short Course:Bayesian Methods for Regression in R from LISA on Vimeo.
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 a subjective versus objective 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?
Before you show up:
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. The pre course assignment is quite long. If there are already parts you are comfortable with, feel free to skip.
Pre-course assignment:
- Refresh basics of probability
- Conditional probability
- Bayes’ Theorem
- Common probability distributions
- Normal
- Gamma (and its special cases)
- Poisson
- Binomial (Bernoulli is a special case)
- Beta
- T
- Uniform
- Not so common probability distributions
- Inverse-Gamma
- Wishart
- Inverse-Wishart
- Dirichlet
- Refresh knowledge of R software language.
- Install R and RStudio on your computer (both free).
- Link to R: http://cran.r-project.org/
- Link to RStudio: http://rstudio.org/
- Install R and RStudio on your computer (both free).