While the topics covered are broad, each week will feature different examples from genetics, ecology, molecular, and evolutionary biology highlighting uses of each individual set of techniques. Each topic will have accompanying readings highlighting a general introduction to a technique (required), and one or more general references or examples. For access to blocked readings, biol609.

To see the code of a lecture, load the slides, then change the file extension in your browser bar to .Rmd to get the code. Or go to this link for all of the code.

Week 1.

1/23/2024
Lecture: Why Advanced Data Analysis?, Introduction to Bayes
Reading: McElreath Ch. 1-2
Deeper Reading: Bayes’ Rules
Even Deeper Reading: Bayesian Basics
Tidying Rethinking: Small Worlds and Large Worlds
Etherpad: https://etherpad.wikimedia.org/p/609-bayes-2024

Week 2.

1/30/2024
Lecture: Sampling from your Posterior
Reading: McElreath Chapter 3, Sampling the Imaginary
Deeper Reading: Approximating the Posterior, Posterior Inference and Prediction
Even Deeper Reading: Are confidence intervals better termed ”uncertainty intervals”?, Assessing uncertainty in physcial constants
Tidying Rethinking: Sampling from your Posterior
Thursday Problems: sampling
Etherpad: https://etherpad.wikimedia.org/p/609-posteriors-2024

Week 3.

2/6/2024
Lecture: Linear Bayesian Models
Reading: McElreath Chapter 4
Deeper Reading: Simple Normal Regression, Posterior Inference and Prediction
Even Deeper Reading: Gavin Simpson on B-Splines, Michael Clark’s GAM Introduction, Resources for Learning about GAMs in R
Tidying Rethinking: Geocentric Models
Etherpad: https://etherpad.wikimedia.org/p/609-linreg-2024
Lab: Linear Bayesian Models

Week 4.

2/20/2024
Lecture: Multiple Predictor Variables
Reading: McElreath Chapter 5, Skim Ch. 6 (we did this in 607)
Deeper Reading: Extending the Normal Regression Model
Tidying Rethinking: The Many Variables and Spurious Waffles
Lab: Multiple Predictors with Rethinking
Etherpad: https://etherpad.wikimedia.org/p/609-multiple_predictors-2024

Week 5.

2/27/2024
Lecture: Bayesian Multimodel Inference
Reading: McElreath Chapter 7
Deeper Reading: Aho et al 2013 Ecology, Gelman et al. 2013 on WAIC and LOO
Tidying Rethinking: Ulysses Compass
Lab: MultiModel Inference with Rethinking
Etherpad: https://etherpad.wikimedia.org/p/609-mmi-2024

Week 6.

3/5/2024
Lecture: Hamiltonian and Markov Chain Monte-Carlo Approaches
Reading: McElreath Chapter 8-9
Deeper Reading: MCMC Under the Hood
Lab: HMC and Interaction Effects
Etherpad: https://etherpad.wikimedia.org/p/609-hmc-2024

Week 7.

Catch Up Labs!

Week 8.

3/19/2024
Lecture: MaxEnt and Bayesian GLMs
Lab: GLM lab
Reading: McElreath Chapter 10-11. Deeper Reading: Poisson and Negative Binomial Regression, Logistic Regression
Files: usethis::use_course("https://biol609.github.io/lectures/glm_data.zip")
Etherpad: https://etherpad.wikimedia.org/p/609-glm-2024

Week 9.

3/26/2024
Lecture: Overdispersed Models, Zero Inflated Models
Reading: McElreath Chapter 12
Further Reading: ver Hoef and Boveng 2007 on overdispersion, Roset et al 2006 on Overdispersion, Zuur chapter on zero inflation Etherpad: https://etherpad.wikimedia.org/p/609-zig_zag-2024

Week 10.

4/2/2024
Lecture: Random Effects, Varying Intercept Mixed Models, Varying Slope Models
Etherpad: https://etherpad.wikimedia.org/p/609-ranef-2024
Reading: McElreath Ch. 13, 14 to 14.3
Further Reading: Gelman and Hill Ch. 12 and/or Zuur on Random Effects, A Practical Guide to Generalized Linear Mixed Models, Gelman and Hill Ch. 12 (see the bit on one model written 5 ways), Gelman and Hill Ch. 13
Writings on visualization: Random regression coefficients using lme4, Making mixed model plots look fancy
A Little More Reading: R2 for mixed models (from Jon Lefcheck), lme4 converge warnings and solutions

Week 11.

4/9/2024
Lecture: Visualizing Complex Bayesian Models
Reading: TBD

Week 12.

4/16/2024
Lecture: Dealing with Autocorrelation with Gaussian Proces Models and GAMs
Reading: McElreath Chapter 14.5 On GP and Ch4 on BSplines
Etherpad: https://etherpad.wikimedia.org/p/609-beyond-2024
Files: predicted GP script
Reading: Spatial autoregressive models for statistical inference from ecological data, McElreath Chapter 14, Roberts et al. 2012, GAMs: an Introduction, Michael Clark’s GAM Introduction, Hierarchical Generalized Additive Models in ecology: an introduction with mgcv
Further Readings: Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Week 13.

4/23/2024
Lecture: Measurement Error, Missing Data, and Bayesian Meta-Analysis
Reading: McElreath Chapter 15.
Homework: Project Proposals Due

Week 14.

4/30/2024
Lecture: Working with advanced models, Bayes Outside of Rethinking

Week 15.

5/7/2024
Final Work Party Week!
Final Presentations: Friday May 10th
Final Papers Due: May 21st