Day 1 (Mon., 6/3)

Session 1 (9 - 12:30): Introduction, Room 345 Smith Natural Resources Building
  1. Course Introduction (Hobbs)
  2. Participant / Instructor Introductions
  3. Rules of Probability (Hobbs)
  4. Probability Lab 1
Session 2 (1:45 - 5:00) Probability Distributions
  1. Continue probability lab
  2. Probability Concepts and Notation (Tang)
  3. Board work: part 1 and part 2
  4. Probability Distributions and R
    1. Solution to barn swallow example
  5. Probability Lab 2
  6. Distribution Cheatsheet

Review Hobbs and Hooten chapters 1-3.

Day 2 (Tues. 6/4)

Session 1 (9 - 12:30): Moment Matching and Likelihood
  1. Marginal Distributions (Ketz)
  2. Probability Lab 3
  3. Moment Matching (Hobbs)
  4. Probability Lab 4
  5. Likelihood (Hooten)

Review Hobbs and Hooten chapters 1-4.

Session 2 (1:45 - 5): Bayes Theorem
  1. Bayes’ Theorem (Hobbs)
  2. Bayes’ Theorem Lab

Review Hobbs and Hooten, chapter 5

Day 3 (Wed. 6/5)

Session 1 (9 - 12:30): Priors and MCMC overview
  1. Priors 1 (Tang)
    1. Live code: knitted file and raw code
  2. Priors Lab 1 10:30—Group Photo Shoot
  3. MCMC 1 (Hobbs)
  4. MCMC Lab 1
  5. MCMC Gibbs Sampling Math
Session 2 (1:30 - 5): MCMC continued (Hooten)
  1. Continue MCMC Lab 1
  2. MCMC 2 (Hooten)
  3. MCMC 2 Lab, svl.dat

Review Hobbs and Hooten Chapters 5 and 7.

Day 4 (Thurs. 6/6)

Day 5 (Fri. 6/7)

Session 1 (9 - 12:30): Bayesian Regression and GLM
  1. Bayesian Regression (Tang)
  2. Lognormal Explanation
  3. Bayesian Regression Lab, Coexistence Data, Abundance Data
Session 2 (1:30 - 5:00 ): Model checking and multi-model inference
  1. Model Checking (Ketz)
  2. Model Checking Lab
  3. Inference from multiple models (Hooten)
  4. Model Selection Lab
  5. Model Selection Lab Math

Review Hobbs and Hooten Chapters 8, 9

Day 6 (6/8) Off.

Day 7 (Sun. 6/9)

(9 - 3): Multi-level modeling
  1. Multi-Level Modeling (Hobbs)
  2. Multi-Level Modeling Lab

Review Hobbs and Hooten Chapter 6

Day 8 (Mon. 6/10)

Session 1 (9 - 12:30): Writing hierarchical models
  1. Writing Hierarchical Models (Tang)
  2. Writing Hierarchical Models Lab

Review Hobbs and Hooten Chapter 6

Session 2 (1:30 - 5:00): More about priors
  1. Priors II (Ketz)
  2. Priors II lab, Ants Data

Day 9 (Tues. 6/11)

Session 1 (9 - 12:30): Mixture models
  1. Mixture Models and Zero Inflation (Hooten)
  2. Mixture models lab
Session 2 (1:30 - 5:00): Missing data
  1. Missing Data and Ignorability (Hobbs)
  2. Subscripts and design matrices handout
  3. Posterior predictive checks with missing data handout
  4. Missing Data Lab

Day 10 (Wednesday 6/12), Concurrent Sessions

Dynamic models (Hobbs)

  1. Dynamic Models Lecture
  2. Lynx Lab

Spatial models (Hooten)

  1. Spatial Models Lecture
  2. Spatial Models Lab, Spatial Data
  3. Spatial Point Process Models Lecture
  4. Spatial Models Lab, Spatial Point Process Data

Review and recap (Tang and Ketz)

Occupancy and capture mark recapture models (Gerber)

  1. Occupancy and Capture-Recapture Lecture
  2. Occupancy Lab, SwissBirds.rda, ClinchDace.rda
  3. Capture-Recapture Lab, sal_data.txt

Day 11 (Thursday 6/13, 9 - 5), Graduation challenge

What you can do for us

Develop a Bayesian model from start to finish

Little Bighorn species richness data

6:00 Party at Hobbs house