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Bayesian Learning Group Summer 2024

August 9, 2024 - Interaction models

Attendance

Agenda

Further reading

Upcoming offerings from CCT Data Science

July 19, 2024 - Interaction models Categorical predictors and model comparison

Attendance

Harsha V

Agenda

July 5, 2024 - Posterior distributions

Attendance

Walter Betancourt
Harsha V
N. Brandon Barba

Agenda

June 21, 2024 - Linear model

Attendance

  • Alex Strong
  • Irina Stefan
  • Salena Ashton
  • Walter

Agenda

June 7, 2024 - Fundamentals

Attendance

-Alex Strong
-Walter Betancourt
-Harsha Vishwanath
-Joshua Oyekanmi
-N. Brandon Barba
Put your name here - optional, but it helps us keep track!

Agenda

May 24, 2024 - Launch, + MCMC

Attendance

Put your name here - optional, but it helps us keep track!

  • Renata Diaz
  • Matthew Cheffer
  • Alex Strong
  • Brandon Barba
  • Salena Ashton
  • Walter Betancourt
  • Harsha Vishwanath
  • Irina Stefan

Agenda

  • Introductions (5m)

    • Please share: Name, pronouns (if you want), department, career stage, any past Bayesian experience, what you're hoping for from the group
  • Group format (10m)

    • Flexible and adaptive - we will make this what you ask for
    • Learning group, not a course
      • No lecturing
      • Encouraged to read or watch materials between sessions
      • In-session, we will work through problems from the book (Statistical Rethinking by Richard McElreath)
    • Model "show and tell"
      • Do you have a model you've worked up? Talk us through it!
      • Sign ups above
    • Other ideas
      • Group modeling projects
  • Resources (5m)

  • Statistics!

    • Orientation (20m)
      • (McElreath's take on) Bayesian approaches
        • Golems
        • DAGs
        • Limits of causal inference
      • Components of a Bayesian model
      • Modeling workflow
        • DAG
        • Write a model
          • Decide on priors
        • Translate to code
        • Run model
        • Investigate posterior
    • MCMC (5m)
      • Examples in the early chapters worked with quap (quadratic approximation)
      • MCMC is an algorithm to sample the posterior more efficiently
      • Discuss MCMC under the hood
    • Revisiting an example from the spring with MCMC (15m)
    • Software install (on your own)

Spring doc

Link to Spring doc