Syllabus

Here is a list of topics that are planned.

Resources

The course will initially use two books to cover the syllabus above. Additional resources will be added when appropriate.

• 1. Statistics, Data Mining, and Machine Learning in Astronomy, 2nd Ed. by Ivezic, Connolly, VanderPlas & Gray.
• 2. Modern Statistical Methods for Astronomy with R Applications by Feigelson & Babu.

The following is a more extended list of relevant resources.

• Books
  
  • Theory:
    
    • All of Statistics: A Concise Course in Statistical Inference by L. Wasserman.
    • In All Likelihood: Statistical Modelling and Inference Using Likelihood by Y. Pawitan.
    • Probability Theory: The Logic of Science by E. T. Jaynes (first three chapters available for free here).
    • The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation by C. P. Robert.
  • Astronomical applications with/without software support:
    
    • Statistics, Data Mining, and Machine Learning in Astronomy by Ivezic, Connolly, VanderPlas & Gray. The authors are part of the LSST collaboration. The online resources to the book are part of the AstroML Python package.
    • Modern Statistical Methods in Astronomy with R Applications by E. Feigelsen & J. Babu.
    • Bayesian Data Analysis by Gelman, Carlon, Stern, Dunson, Vehtari, & Rubin (book website links to course notes as well as solutions to selected problems.)
    • Bayesian Logical Data Analysis for the Physical Sciences by P. C. Gregory.
    • Bayesian Methods for the Physical Sciences by S. Andreon & B. Weaver.
    • Statistical Inference by G. Casella & R. L. Berger ( Solutions problems in the book).
    • Statistical Methods for Astronomical Data Analysis by A. K. and T. Chattopadhyay.
    • Practical Statistics For Astronomers by J. Wall & C. Jenkinsi (website also links to lectures and solutions).
  • Special topics:
    
    • Bayesian Spectrum Analysis and Parameter Estimation by G. L. Bretthorst.
    • A review of MCMC methods and their applications to astronomical problems by S. Sharma.
• Papers
  
  • Error estimation in astronomy: A guide (R. Andrae)
  • David Hogg's 2010 extremely informative paper on fitting a line to data
  • Tom Laredo's 1990 article, "From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics"
  • Understanding (some) astronomical data better using Bayesian methods (S. Andreon)
  • Fitting a power law to data: caveats






• Miscellaneous articles
  
  • ArXiV minimal stats checklist (Johannes Buchner)
  • Harrison et al. (2015) paper on a method to extend a KS-like test to multiple dimensions.
  • A note on probabilistic computation.




• Conferences and websites
  
  • The Astrostatistics Facebook Group
  • The International Astrostatistics Association Facebook Group
  • The Penn State Astrostatistics Webpage (includes links to the summer schools on astrostatistics and astroinformatics.)
  • The 2017 TIARA Astrostatistics Summer School at ASIAA, Taiwan