These course materials were developed under NSF awards DEB-1145200, DBI-1052875 and DBI-163914.

Day 1: (6/3) Probability
  1. Participant and Course Introduction
  2. What Sets Bayes Apart?
  3. Differences among the three branches of statistics illustrated with confidence envelopes
  4. Rules of Probability & Probability Lab 1
  5. Probability Concepts and Notation
  6. Probability Distributions & Probability Lab 2
  7. Probability Distributions Boardwork
  8. Distributional Cheatsheet
  9. Welcome Happy Hour at SESYNC!

Review Hobbs and Hooten chapters 1-3.


Day 2: (6/4) Likelihood & Bayes’ Theorem
  1. Marginal Distributions & Probability Lab 3
  2. Moment Matching & Probability Lab 4
  3. Likelihood & Lab
  4. Likelihood Lab Excel File & Answer

Review Hobbs and Hooten chapters 3.5-4.


Day 3: (6/5) Intro to Bayesian Statistics & Markov Chain Monte Carlo
  1. Likelihood Lab Continued
  2. Bayes’ Theorem & Lab
  3. More about Priors I & Lab
  4. Beta-binomial Conjugacy Derivation

Review Hobbs and Hooten chapters 5 and 7.


Day 4: (6/6) JAGS and Model Inference
  1. MCMC Gibbs Sampling & MCMC Lab 1
  2. MCMC Gibbs Sampling Math
  3. JAGS Primer & Lab Answers
  4. MCMC Metropolis-Hastings (optional) & MCMC Lab 2 (optional)

Review Hobbs and Hooten chapter 7-8.


Day 5: (6/7) Bayesian Regression & Multi-Level Modeling
  1. JAGS Primer Continued & Lab Answers
  2. JAGS Problems Lab
  3. Inference From a Single Model
  4. Bayesian Regression
  5. Lognormal Explanation

Review Hobbs and Hooten chapter 6.


Day 6: (6/8) Off


Day 7: (6/9) Multi-Level Modeling
  1. Multi-Level Modeling
  2. Multi-Level Modeling Lab

Review Hobbs and Hooten chapters 6, 10-12.


Day 8: (6/10) Multi-Level Modeling and Model Checking
  1. Multi-Level Modeling Lab Continued
  2. Model Checking & Lab
  3. Writing Hierarchical Models & Lab

Review Hobbs and Hooten chapters 6, 8, 10-12.


Day 9: (6/11) Writing Hierarchical Models and Mixture Models
  1. Writing Hierarchical Models Lab Continued
  2. More About Priors II
  3. Mixture Models, Zero Inflation & Occupancy & Lab


Day 10: (6/12) Occupancy Models, Ordinal Models & Model Selection
  1. Swiss Birds Lab
  2. Ordinal Models & Lab
  3. Model Selection & Lab
  4. Model Selection Lab Math


Day 11: (6/13) Dynamic Models & Spatial Modeling
  1. Course Evaluation Survey
  2. Dynamic Models & Lynx Lab
  3. Spatial Modeling for Continuous Data & Spatial Lab
  4. Spatial Modeling for Areal Data (optional)
  5. Farewell Happy Hour at SESYNC!


Topics We Did Not Get To Cover
  1. Designed Experiments
  2. Missing Data and Ignorability