Materials related to my undergraduate courses can be found on Portal. This page only contains material related to my graduate courses.

Winter 2018: Mini-course on Statistics and Inference in Astrophysics


Syllabus (pdf)

See below for some further resources.

Lecture slides

Lecture 1
Lecture 2 (Fitting example)
Lecture 3 (MCMC demo, MCMC examples notebook)
Lecture 4

Problem set

Problem Set: the Hubble constant from the local distance ladder

Fall 2017: AST1420: Galactic Structure and Dynamics

See the course website on GitHub

Winter 2016: Mini-course on Statistics and Inference in Astrophysics

Lecture notes

Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 5

Problem sets

Problem Set 1
Problem Set 2

Some resources

Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data textbook
Figures from the textbook (with code)
Information Theory, Inference, and Learning Algorithms, by David Mackay; see Part IV in particular
Gaussian Processes for Machine Learning, by Carl Edward Rasmussen and Christopher K. I. Williams
Data analysis recipes: Fitting a model to data
Data analysis recipes: Probability calculus for inference
More to be added as the course progresses

Creative Commons License
The lecture notes on this page are licensed under a Creative Commons Attribution 4.0 International License. Lecture notes on external websites linked to from this page are covered under licenses specified on those websites.

contact info

email: bovy [at] astro.utoronto.ca

Department of Astronomy and Astrophysics
University of Toronto
50 St. George Street
Toronto, ON M5S 3H4