Mini course on Statistics in Astronomy (2013)
Syllabus
 Lectures
 Jan 25–Feb 15: F11, AB 113; after reading week: TBD.
 Lecturers
 Barth Netterfield and Marten van Kerkwijk
 Web page

http://www.astro.utoronto.ca/~mhvk/STATMINI/
 Course texts
 As given below, but a general reference would be Bayesian Logical Data Analysis for the Physical Sciences (BLDAPS), by Phil Gregory (2005, Cambridge Univ. Press). This text also describes nonBayesian analysis and shows how for quite general cases the results are very similar. See also a pdf scan of Marten's notes.
 Evaluation

For students taking this course for credit: the
testing will be by two assignments.
 Problem set 1 (pdf), due 1 March 2013
 To come
Schedule
Fri, Jan 25 (Marten)
Literature: Numerical Recipes, parts of Chapter 15; BLDAPS 5, 6
 General error propagation.
 Introduction to χ^{2} fitting, probabilities, number of parameters, degrees of freedom. Estimating expected uncertainties.
 Applications: straight line, etc.
Fri Feb 1 (Barth)
 Introduction to Bayesian analysis; (1 parameter), priors.
 Relation with "frequentist" approach.
Fri Feb 8 (Marten;
Literature: Horne 1986PASP…98..609H: An optimal extraction algorithm for CCD spectroscopy.
 Determining the optimal way to extract data; thinking clearly about what is actually measured.
 Application to images and spectra.
Fri Feb 15 (Marten)
Literature: Numerical Recipes, remainder of Chapter 15; Alard & Lupton, 1998ApJ…503..325A: A Method for Optimal Image Subtraction; Rucinski 2002AJ….124.1746R: Radial Velocity Studies of Close Binary Stars. VII. Methods and Uncertainties.
 General leastsquares modelling with base functions.
 Leastsquares fitting algorithms
 Applications: optimal image subtraction, rotational line profiles
Fri Mar 1 (Barth)
 Non Gaussian likelihoods and error estimates.
 Significance estimates.
Fri Mar 8 (Marten)
Literature: Cash 1979ApJ…228..939C: Parameter estimation in astronomy through application of the likelihood ratio; for a Bayesian perspective, Gregory & Loredo 1992ApJ…398..146G: A new method for the detection of a periodic signal of unknown shape and period.
 Poisson errors, maximum likelihood for Poissondistributed data.
 Pitfalls: Resolution and binning (e.g., for Xray spectra).
 Pitfalls: number of trials (e.g., source/period finding).
Fri Mar 15 (Barth)
 Multi Parameter Baysian, Fischer matrix.
 Correlated parameters, marginalization.
Fri Mar 22 (Barth)
 Monte Carlo analysis, error estimates.
 Relation to Baysian analysis.
 More pitfalls??
Date: 20130222 14:32:02 EST
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