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Computation infrastructure for
teaching Bayesian modeling

Colin Rundel

Univ of Edinburgh
Duke University

1 / 8

Context

Sta 444 / 644 - Spatiotemporal Modeling

  • 4th year undergraduate elective (2nd year MS elective)

  • Prereq Sta 360 - Bayesian Inference and Modern Statistical Methods

  • Weekly labs / problem sessions with TA

  • 5 hws + 1 group project over the semester
    (mathematical, computational, and applied problems)

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Learning Outcomes

  • Modeling methods (lm, glm, ARIMA, GPs, CAR, etc.)

  • Model assessment and validation

  • Bayesian model implementation (probabilistic programming)

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DLM for predicting CO2

Violent Crime in Baltimore City

4 / 8

Software

  • Wide varierty of choices: BUGS, Jags, Stan, etc.

  • Some considerations:

    • Generalizability vs. specificity

    • Syntactic complexity

    • Performance

    • Limitations

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Computational Complexity and Efficiency

This is often the first course where students engaged with models that cannot be fit "instantly".

  • Basics of algorithmic complexity

  • Model limitations vs software limitations

  • Implementation vs run time

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Infrastructure

The platform provided for students matter (labs vs. servers vs. student laptops):

  • Configuration / Administration

  • Performance

  • Workflows

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Context

Sta 444 / 644 - Spatiotemporal Modeling

  • 4th year undergraduate elective (2nd year MS elective)

  • Prereq Sta 360 - Bayesian Inference and Modern Statistical Methods

  • Weekly labs / problem sessions with TA

  • 5 hws + 1 group project over the semester
    (mathematical, computational, and applied problems)

2 / 8
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