Course Topics

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An outline for questions I hope to answer:

What is Bayes’ Rule? (lecture portion)

â–ºÌýWhat is the likelihood?

â–ºÌýWhat is the prior distribution?

â–ºÌýHow should I choose it?

â–ºÌýWhy use a conjugate prior?

â–ºÌýWhat is a subjective versus objective prior?

â–ºÌýWhat is the posterior distribution?

â–ºÌýHow do I use it to make statistical inference?

â–ºÌýHow is this inference different from frequentist/classical inference?

â–ºÌýWhat computational tools do I need in order to make inference?

How can I use R to do regression in a Bayesian paradigm? (computer portion)

â–ºÌýWhat libraries in R support Bayesian analysis?

â–ºÌýHow do I use some of these libraries?

â–ºÌýHow do I interpret the output?

â–ºÌýHow do I produce diagnostic plots?

â–ºÌýWhat common topics do these libraries not support?

â–ºÌýHow can I do them myself?

â–ºÌýHow can LISA help me?

â–ºÌýWhat 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:Ìý
      • Link to RStudio:Ìý