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ASTRON 193 - Noise and Data Analysis in Astrophysics or return to Course Catalog Search

114603 – Section 001   

SchoolDepartmentFaculty
Faculty of Arts and SciencesAstronomyAneta Siemiginowska and Vinay Kashyap
TermDay and Time
Spring 2016-2017  (show academic calendar)MW   2:00 p.m. - 3:29 p.m.
Credits
4  (show credit conversion for other schools)
Credit Level
Graduate and Undergraduate

Description
How to design experiments and get the most information from noisy, incomplete, flawed, and biased data sets. Basic of Probability theory; Bernoulli trials: Bayes theorem; random variables; distributions; functions of random variables; moments and characteristic functions; Fourier transform analysis; Stochastic processes; estimation of power spectra: sampling theorem, filtering; fast Fourier transform; spectrum of quantized data sets. Weighted least mean squares analysis and nonlinear parameter estimation. Bootstrap methods. Noise processes in periodic phenomena. Image processing and restoration techniques. The course will emphasize a Bayesian approach to problem solving and the analysis of real data sets.

Prerequisite(s)
Prerequisite: Mathematics 21b

Notes
This course offered alternate years.

Exam Group
FAS14_D

 
Cross Registration
Eligible for cross-registration
With permission of instructor/subject to availability

MIT students please cross register from MIT's Add/Drop application.

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