Lessons

Lesson 0: Configuring your computer
Lesson 1: Pandas and split-apply-combine
Lesson 2: Exploratory plotting
Lesson 3: Probability distributions and the plug-in principle
Lesson 4: Nonparametric inference with hacker stats
Lesson 5: Generative modeling and parametric inference
Lesson 6: Maximum likelihood estimation
Lesson 7: Variate-covariate modeling
Lesson 8: Model assessment

Exercises

Exercise 1. Pandas and split-apply-combine
Exercise 2. Exploratory plotting
Exercise 3. Working with probability distributions
Exercise 4: Nonparametric hacker stats
Exercise 5: Generative modeling
Exercise 6: Maximum likelihood estimation
Exercise 7: MLE with variate-covariate models
Exercise 8: Model assessment

Schedule

Schedule overview
Daily schedule

PoL workshop on statistical inference

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Exercise 6: Maximum likelihood estimation
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Exercise 6: Maximum likelihood estimation

Exercise 6.1: Modeling and parameter estimation for Boolean data
Exercise 6.2: The Olkin-Petkau-Zidek example of MLE fragility
Exercise 6.3: Heavy-tailed distributions and outliers

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Last updated on Jul 07, 2022.

© 2021 Justin Bois and BE/Bi 103 a course staff. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.

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