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Week 1 (end of August)
Week 1 (end of August)
Readings and things to do: Syllabus, Jeffrey (chapters 1-5), and R setup with Rstudio
Objective: Introduce course, probability theory, and R
Optional but strongly recommended: Rstudio install, R tutorial #1, R tutorial #2, R tutorial #3, How to become a Bayesian, and for the really ambitious R for Data Science
Week 2 (1st week of Sept)
Week 2 (1st week of Sept)
Readings and things to do: Count Bayesie - Bayes’ theorem with Lego
Objejctive: Bayes theorem, subjective probability, and updating priors
Optional but strongly recommended: Kahn Academy Probability and Combinatorics, Bayes Theorem video, and this video
Week 3 (2nd week of Sept)
Week 3 (2nd week of Sept)
Readings and things to do: Bayesian Essentials with R (pages 1-61) requires GMU login
Objejctive: Normal distribution theory
Optional but strongly recommended: Solutions to problems, bayess package
Week 4 (3rd week of Sept)
Week 4 (3rd week of Sept)
Readings and things to do: The Likelihood and Updating your priors
Objejctive: Simple(r) distributions and update functions
Optional but strongly recommended: Plot different distributions
Week 5 (4th week of Sept)
Week 5 (4th week of Sept)
Readings and things to do: Graphics in R using ggplot2
Objejctive: Better displays
Optional but strongly recommended: Hadley Wickham’s Book and his free (as in beer) R for Data Science book
Week 6 (last week of Sept)
Week 6 (last week of Sept)
Readings and things to do: Flam (2014), Cartoon Explanation, Little (2005), and Meehl (1997)
Objejctive: Frequentists vs. Subjectivists
Optional but strongly recommended: Select from the host of Goodman articles listed here
Week 7 (1st week of Oct)
Week 7 (1st week of Oct)
Readings and things to do: Spiegelhalter & Rice (2009)
Objejctive: Bayesian methods - from start to finish
Optional but strongly recommended: Stan tutorial, Simple English
Week 8 (2nd week of Oct)
Week 8 (2nd week of Oct)
Readings and things to do: Baath (2013) & Baath (2013a)
Objejctive: Correlations, predictions, and spurious relationships
Optional but strongly recommended: Spurious correlations
Week 9 (3rd week of Oct)
Week 9 (3rd week of Oct)
Readings and things to do: Bayesian Essentials (pages 65-137)
Objejctive: Linear models with Bayesian methods
Optional but strongly recommended: Bayesian Linear Regression without tears in R
Week 10 (4th week of Oct)
Week 10 (4th week of Oct)
Readings and things to do: Gelman (2011) and Morey (2016)
Objejctive: Evidence and Bayes’ Factor
Optional but strongly recommended: Replicability-Index, Goodman (1999), and Johnson (2013), Feynman (1974)
Week 11 (1st week of Nov)
Week 11 (1st week of Nov)
Readings and things to do: Bayesian Computation with Stan and Farmer Jöns
Objejctive: Stan
Optional but strongly recommended: Choose from a host of tutorial papers
Week 12 (2rd week of Nov)
Week 12 (2rd week of Nov)
Readings and things to do: Introduction to Bayesian Inference (with Python)
Objejctive: Python applications
Optional but strongly recommended: Think Bayes - long but worth reading throughout the semester
Week 13 (3rd week of Nov)
Week 13 (3rd week of Nov)
Readings and things to do: Peruse the Bayesian Inference Task View on the CRAN
Objejctive: Using R packages for Bayesian methods
Optional but strongly recommended: Try BayesianTools, Using R for Bayesian Statistics
Week 14 (Thanksgiving Break)
Week 14 (Thanksgiving Break)
Readings and things to do: NONE
Objejctive: Rest and relax with family
Optional but strongly recommended: Tell someone how much they made your life better.
Week 15 (end of semester)
Week 15 (end of semester)
Readings and things to do: Replication of Psychological Science, Estimating the reproducibility of psychological science
Objejctive: Replication and Reproducibility of Science
Optional but strongly recommended: Bayesian meta-analysis in R
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