Tentative Schedule

Week 1 (08/17 & 08/19)

Lectures:

• Monday: Foundations of Probability Distributions .qmd

• Wednesday: Thinking Like a Bayesian (Part 1) .qmd

Week 2 (08/24 & 08/26)

Lectures:

• Monday: Thinking Like a Bayesian (Part 2) .qmd

• Wednesday: Beta-Binomial Model .qmd

Week 3 (08/31 & 09/02)

Lectures:

• Monday: Conjugate Families .qmd

• Wednesday: Balance and Sequentiality - read Chapter 4 of Bayes Rules! at home

Week 4 (09/07 & 09/09)

Activities: Balance and Sequentiality

Week 5 (09/14 & 09/16)

Lectures:

• Monday: Simulation of the Posterior .qmd

• Wednesday: Simulation of the Posterior .qmd

Week 6 (09/21 & 09/23)

Lectures:

• Monday: Bayesian Inference .qmd

EES Project:

• Wednesday: introduce project

  • Begin “project notebook”

  • Get data imported to R

  • Link research objectives to variable names in dataset

  • Begin recording questions for collaborator

Week 7 (09/28 & 09/30)

Activity:

• Monday: Exploring Bayesian Inference (see Canvas)

Lectures:

• Wednesday: Bayesian Generalized Linear Models.qmd

Week 8 (10/05 & 10/07)

In Class Project 1:

• Project is assigned and collaborative work time will be provided on Monday.

• Brief oral presentations (with slides) will be given on Wednesday.

EES Project:

• Your collaborator should be reaching out to schedule a meeting for next week.

  • In this meeting, they will formally introduce you to the project.

  • You will also discuss their research objectives and your initial thoughts on data analysis.

    • Use this meeting to verify that you understand what questions you should be answering with a statistical model.

EES Project:

• Numerical and graphical summaries of data

Week 9 (10/12 & 10/14)

Take-home midterm opens Monday and is due Friday.

Dr. Seals will be available during class time on Monday and Wednesday to answer questions.

EES Project:

• You are meeting your collaborator this week to discuss their project and your initial thoughts on data analysis.

Week 10 (10/19 & 10/21)

Lectures:

• Bayesian Hierarchical Models: Random Intercepts

• Bayesian Hierarchical Models: Random Slopes

EES Project:

• You should be working on the analysis for your collaborator

Week 11 (10/26 & 10/28)

Lectures:

• Bayesian Hierarchical Models:

• Communicating Results and Writing Statistical Methods

EES Project:

• You should be working on the analysis for your collaborator

Week 12 (11/02 & 11/04)

EES Project:

• Individual meetings with Dr. Seals

• Your collaborator should be reaching out to schedule a meeting for next week.

  • In this meeting, you will present your analysis results to your collaborator.

    • Use this meeting to communicate the model results in their language.

Week 13 (11/09 & 11/11)

EES Project:

• You are meeting your collaborator this week to discuss your analysis and results.

Week 14 (11/16 & 11/18)

In Class Project 2:

• Project is assigned and collaborative work time will be provided on Monday.

• Brief oral presentations (with slides) will be given on Wednesday.

EES Project:

• Any necessary updates to your analysis must be completed and sent to your collaborator this week.

• Communicate with your collaborator to schedule a meeting for the week after Thanksgiving (week of 11/30)

Week 15 (11/30 & 12/02)

Take-home final opens Monday and is due Friday.

Dr. Seals will be available during class time on Monday and Wednesday to answer questions.

EES Project:

• You are meeting your collaborator this week to discuss your analysis and results.

Exam Week

EES Project:

• Collaborative final paper due.