Probabilistic decision-making, data analysis, and discovery in astronomy

author: David W. Hogg, Center for Cosmology and Particle Physics, Department of Physics, New York University (NYU)
published: Jan. 15, 2013,   recorded: April 2012,   views: 3827


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 52:38
Watch Part 2
Part 2 42:08


Astronomy is a prime user community for machine learning and probabilistic modeling. There are very large, public data sets (mostly but not entirely digital imaging), there are simple but effective models of many of the most important phenomena (stars, quasars, and galaxies), and there are very good models of telescopes, cameras, and detectors. I will show in detail some examples of problems we were able to solve in astrophysics by bringing probabilistic inference and decision theory to astronomy. I will discuss why many "supervised" methods are not nearly as useful in astronomy as those that involve generative modeling. I hope to leave the audience with real research problems, the solutions to which would be (a) achievable with contemporary machine-learning methods, and at the same time (b) very exciting within the astrophysics community.

See Also:

Download slides icon Download slides: mlss2012_hogg_astronomy.pdf (21.2┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: