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
- Check in on software installs
- Did you have a chance to read or watch the lectures?
- Put an x here for yes:xxx
- Put an x here for no:xx
- What questions did you have after going through the materials?
- Core idea from these chapters:
- Use of DAGs to organize a modeling workflow
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- Directed acyclic graph
- Define your mental model of how the variables in a system influence each other
- Use this to develop a logical generative model (and protect yourself against confounds, etc)
- From the lectures:
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- Components of a Bayesian model
- Exercises from Ch. 2 - see code
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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
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Resources (5m)
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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
- 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