Logo

Lessons

  • 0. Preparing computing resources for the course
  • 1. The cycle of science
  • 2. Version control with Git
  • 3. Introduction to Python
  • 4. Style
  • 5. Test-driven development
  • 6. Exploratory data analysis, part 1
  • 7. Exploratory data analysis, part 2
  • 8. Data file formats
  • 9. Data storage and sharing
  • 10. Data wrangling
  • E1. To be completed after lesson 10
  • 11. Intro to probability
  • 12. Random number generation
  • 13. Probability distributions
  • E2. To be completed after lesson 13
  • 14. Plug-in estimates and confidence intervals
  • 15. Nonparametric inference with hacker stats
  • E3. To be completed after lesson 15
  • 16. Null hypothesis significance testing
  • 17. Hacker’s approach to NHST
  • 18. Parametric inference
  • 19. Numerical MLE
    • Method of moments
    • Numerical maximum likelihood estimation
  • E4. To be completed after lesson 19
  • 20. Variate-covariate modeling
  • 21. Confidence intervals of MLEs
  • 22. Implementation of MLE for variate-covariate models
  • E5. To be completed after lesson 22
  • 23. Mixture models
  • 24. Model assessment
  • 25. Implementation of model assessment
  • E6. To be completed after lesson 25
  • 26. Statistical watchouts
  • 27. Reproducible workflows
  • 28. The paper of the future

Homework

  • 1. Practice with Python tools and EDA I
  • 2. Exploratory data analysis II
  • 3. Wrangling, EDA III, and probability distributions
  • 4. Working with probability distributions
  • 5. Nonparametric hacker stats
  • 6. Maximum likelihood estimation I
  • 7. Maximum likelihood estimation II
  • 8. Maximum likelihood estimation III
  • 9. Model assessment
  • 10. Maximum likelihood estimation IV

Schedule

  • Schedule overview
  • Homework due dates
  • Exam dates
  • Lesson exercise due dates
  • Weekly schedule

Policies

  • Meetings
  • Lab sessions
  • Submission of assignments
  • Lessons and lesson exercises
  • Homework
  • Exams
  • Grading
  • Collaboration policy and Honor Code
  • Excused absences and extensions
  • Course communications
  • “Ediquette”
BE/Bi 103 a
  • 19. Numerical MLE
  • View page source

19. Numerical MLE

  • Method of moments
  • Numerical maximum likelihood estimation
Previous Next

Last updated on Nov 20, 2024.

© 2019–2024 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.



Built with Sphinx using a theme provided by Read the Docs.