Quantifying uncertainty
Master
In Maynard (USA)
Description
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Type
Master
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Location
Maynard (USA)
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Start date
Different dates available
The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering. In this course, we study advanced methods to represent, sample, update and propagate uncertainty. This is a "hands on" course: Methodology will be coupled with applications. The course will include lectures, invited talks, discussions, reviews and projects and will meet once a week to discuss a method and its applications.
Facilities
Location
Start date
Start date
Reviews
Course programme
Lectures: 1 session / week, 2 hours / session
Permission of instructor.
The specific topics vary between offerings, but are generally drawn from the following:
Bryson, Jr. Arthur E. and Yu-Chi Ho. Applied Optimal Control: Optimization, Estimation and Control. Taylor & Francis, 1975. ISBN: 9780891162285.
Gelb, Arthur. Applied Optimal Estimation. MIT Press, 1974. ISBN: 9780262570480.
Gelman, A., J. Carlin, et al. Bayesian Data Analysis. Chapman and Hall, 2003. ISBN: 9781584883883.
Martinez, W. L., and A. R. Martinez. Computational Statistics Handbook with MATLAB. 2nd ed. Chapman and Hall/CRC, 2007. ISBN: 978158488566. [Preview with Google Books]
Papoulis, A. Probability, Random Variables & Stochastic Processes. McGraw-Hill, 2002. ISBN: 9780071226615.
Silverman, B. W. Density Estimation for Statistics and Data Analysis. Chapman and Hall/CRC, 1986. ISBN: 9780412246203.
Double Pendulum.
The course grade is based upon paper explanation, project, and class participation.
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Quantifying uncertainty