Date  Topics  Reading  Assignments 
1/18  Introduction to modelbased inference  Clark: Chapter 1 Optional: Otto and Day: Math Review Lecture Notes 

1/20  Probability theory: joint, conditional, and marginal distributions  Hilborn and Mangel Ch 3 p3962 Optional: Clark Appendix D Lecture Notes 

1/23  Probability theory: discrete and continuous distributions  Hilborn and Mangel Ch 3 p6293 Optional: Clark Appendix F Lecture Notes 

1/25  Maximum Likelihood  Chapter 3.13.2 Optional: Chapter 2 Lecture Notes 

1/27  Point estimation by MLE  Chapter 3.33.5 Lecture Notes 

1/31  Analytically tractable MLEs  Chapter 3.63.9
Optional: Bolker Ch 3 Lecture Notes 

2/1  Intractable MLEs and basic numerical optimization  Chapter 3.103.13
Lecture Notes 

2/3  EXAM 1: Probability Theory, Maximum Likelihood 

2/6  Bayes Theorem  Chapter 4.1 Ellison 2004 Lecture Notes 

2/8  Point estimation using Bayes  Chapter 4.2
Lecture Notes 

2/10  Analyticallytractable Bayes: conjugacy and priors  Chapter 4.3, Appendix G
Lecture Notes 

2/13  Numerical methods for Bayes: MCMC  Chapter 7.17.2, 7.3 intro
Lecture Notes 

2/15  MCMC: MetropolisHastings  7.3.1, 7.3.2, 7.5
Lecture Notes 
Project Proposals 
2/17  MCMC: Gibbs sampler  Chapter 7.3.3, 7.3.4
Lecture Notes BUGS code 

2/20  Interval Estimation: Bayesian credible intervals  Chapter 5
Lecture Notes 

2/22  Frequentist confidence intervals I  Chapter 5
Lecture Notes 

2/24  Frequentist confidence intervals II  Chapter 5
Lecture Notes 

2/27  EXAM 2: Bayes, CI 

2/29  Model Selection: Likelihood ratio test, AIC  Hilborn and Mangel Chapter 2
Lecture Notes 

3/2  Model Selection: DIC, predictive loss, model averaging  Chapter 6
Lecture Notes 

3/5  Regression  Chapter 5.4 & 7.4
Lecture Notes 

3/07  Regression: Errors in variables, heteroskedasticity  Chapter 7.6, 7.7, 8.1
Lecture Notes 

3/09  Logistic regression  Chapter 8.28.2.3
Lecture Notes 

3/12  GLMs  Chapter 8.28.2.3  
3/14  Mixed Models  Chapter 8.2.4 Lecture Notes 
Model Description 
3/16  Regression  Lecture Notes R demo 

3/26  Hierarchical Bayes  Chapter 8.2.5  8.3 Lecture Notes 

3/28  Nonlinear models  Chapter 8.4 Lecture Notes 

3/30  Applications of random effects models  Chapter 8.58.7 Lecture Notes 

4/2  EXAM 3 GLMM, HB 

4/4  Time series: Basics and StateSpace  Chapters 9.1, 9.2, 9.6 Lecture Notes 

4/6  Time series: State Space and MarkRecapture  Chapter 9.7, 9.8, 9.16 Lecture Notes 

4/09  Time series: ARMA  Chapter 9.3, 9.5 Lecture Notes 

4/11  Time Series: Repeated Measures  Chapter 9.10, 9.14, 9.15 Lecture Notes 
Preliminary Analysis 
4/13  Time series: Matrix models, SIR  Chapter 9.17, 9.18 Hatala et al. 2011 

4/16  Spatial: point pattern data  Chapter 10.6 Lecture Notes 

4/18  Spatial: pointreferenced (geostatistical) data and Kreiging  Chapter 10.7 Lecture Notes 

4/20  Spatial: blockreferenced data and misalignment  Chapter10.8 Lecture Notes 

4/23  Spatial: conditional autoregressive models (CAR)  Chapter 10.9, 10.10 Lecture Notes 

4/25  Data assimilation: classic Kalman filter  Wikle and Berliner 2007  
4/27  Data assimilation: Kalman variants  
4/30  Data assimilation: Bayesian statespace revisited  
5/2  Forecasting: Ensemble analysis  
5/8 8 AM 
EXAM 4 Time, Space, DA 211 Davenport Hall 
FINAL PROJECT 
Lab  Week  Topics  Software 
1  1/18  Introduction to R  R 
2  1/25  Probability distributions and sampling  R 
3  2/1  Maximum likelihood  basics  R 
4  2/8  Maximum likelihood  numerical optimization  R 
5  2/15  Introduction to BUGS  BUGS 
6  2/22  Gibbs sampler  R 
7  2/29  Metropolis Algorithm  R 
8  3/7  Interval estimation and model selection  R 
9  3/14  Regression  Both 
10  3/28  Hierarchical modeling  WinBUGS 
11  4/4  Statespace time series  WinBUGS 
12  4/11  Peer Assessment  
13  4/18  Exploratory data analysis: space and time  R 
14  4/25  Spatial CAR and Kriging  WinBUGS 
15  5/2  Data Assimilation  R 