SCS Reads 2015-2016

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Contents

Getting started

Week 1 (Oct. 9)

We are reading the very short Chapter 1 from Kaplan

  • BayesBalls.R Animation of Bayes billiard thought experiment

Week 2 (Oct. 23)

  • Bernoulli distribution example--- how to specify a beta(a, b) prior as prior mean and effective sample size: bern-beta-ex.pdf (corresponding R code: bern-beta-ex.R)
    This uses some R functions from Doing Bayesian Data Analysis, [DBDA software page]

Week 3 (Nov. 6)

  • Kaplan, Ch 3. Another ho-hum chapter, giving results for priors and posteriors for a variety of standard distributions without much

insight. Would make a reasonable Wikipedia page.

  • Demonstration of Poisson distribution with Gamma prior (Fig 3.4) poisson.pdf (corresponding R code: poisson.R)

Week 4 (Nov. 20)

  • An R function to illustrate a bivariate Gibbs Sampler for normal distribution: biNormGibbs.R

Week 5 (Dec. 4)

  • Reading: Kaplan Chapter 5, Bayesian Hypothesis Testing
  • JAGS analysis of simple linear regression jags-ex1.R
Output: jags-ex1.pdf
Output: bayes-factor-ex.pdf

Week 6 (Jan. 15)

  • Reading: Kaplan Chapter 6, Bayesian Linear and Generalized Linear Models

Week 7 (Jan. 29)

  • Reading: Kaplan Chapter 7, Missing data from a Bayesian perspective

Week 8 (Feb. 12)

  • Reading: Kaplan Chapter 8, Bayesian multilevel modeling

Week 9 (March 4)

  • Reading: Kaplan Chapter 9, Bayesian modeling for continuous and categorical latent variables

Week 10 (March 18)

Week 11 (April 1)

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