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Notes

This is a collection of notes about various science and programming topics of interest to me. It’s mostly here so I can reuse the stuff I write for students and collaborators, but if anyone else gets use out of it that’s great too. For good measure I’ve mixed in external references that I found useful, marked with ⇱. Let me know if you spot any errors.

Scientific Python

• Numpy – An introduction to Numpy, the core Python package for scientific computing. It assumes you know basic Python.
• Python Packages – How to create a Python package, including file structure and the setup system.

Statistics and Math

• ⇱ Matrix Cookbook (Book) – A free reference ebook by Peterson and Pedersen, especially helpful for multivariate statistics.
• ⇱ Gaussian Processes (Book) – A free (e)book by Rasmussen and Williams that does a great job covering the topic.
• ⇱ Conjugate Priors (Wikipedia) – An underappreciated gem covering this core part of analytical Bayesian methods; the table at the end is excellent.
• ⇱ Catalog of Noninformative Priors – a list of uninformative priors for a rather large list of models, sometimes listing the resulting posterior.