Stats Lectures

Introduction to Statistics for Astronomy and Physics

NEW: We have a YouTube Channel!!
Click here to see each and every stat lecture online!

What: The purpose of these lectures is to familiarize graduate students with the type of techniques that they will likely employ as graduate students and encounter in other work in the field. These lectures are meant to give practical information and advice on the meaning and implementation of various important topics in statistics as they apply to physical and astronomical data sets. Anyone interested in working with data, as well as everyone who has interest in this important topic, is encouraged to attend.

Where: NYU Center for Cosmology and Particle Physics, Meyer Hall, 4 Washington Pl, in the 5th floor lounge.

When: See detailed schedule below, but we plan to have lectures at 9:30AM (most) every Friday morning.

Breakfast mixers: A few times during the semseter, there will be coffee, bagels, granola, etc, for the graduate students from the different institutions to mingle and get to know one another. This will start at 9:00AM, with the first mixer being Feb. 15. See the schedule before for the lectures with the mixer. The exact dates will be decided as the semester goes on.

  • Michael Blanton
  • Kyle Cranmer
  • David W. Hogg
  • Maryam Modjaz
  • Jeremy Tinker

Here is the list of lectures from the Winter 2013 series. You can see all these lectures on the YouTube channel listed above. How:
  • Fitting Linear Models with Gaussian Errors (Feb 8; MB)
  • Properties of estimators, confidence intervals, hypothesis tests, etc. (Feb 15; KC) mixer!
  • Likelihood-based inference / statistical tests (Feb 22; KC) mixer!
  • Correlation Functions, Bootstrap/Jackknife errors, covariance matrices (Mar 1; JT) mixer!
  • Markov Chain Monte Carlo (Mar 8; JT)
  • Model Selection and Cross-Validation (Mar 15; DWH)
  • NYU Spring Break (Mar 22)
  • [unscheduled] (Mar 29)
  • Image Analysis, with Treatment of Convolution (Apr 5; MB)
  • Dimensionality Reduction (Apr 12; DWH)
  • A Quick Tour of Machine-Learning and Statistical tools (Apr 19; DWH)
  • [unscheduled] (Apr 26)