Difference between revisions of "CEM"
From Maxwell
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| − | <font color='blue' size='+2'>Introduction to | + | <font color='blue' size='+2'>Introduction to Computational Electromagnetics</font>__NOTOC__ |
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'''Instructors''' | '''Instructors''' | ||
| − | * | + | * Dániel Marcsa (CDS/BE) |
| − | * Lectures: | + | * Lectures: Monday, 14:50 - 16:25 (D201), 16:30 - 17:15 (D105) |
* Office hours: by request | * Office hours: by request | ||
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'''Teaching Assistants''' | '''Teaching Assistants''' | ||
| − | * | + | * - |
| − | * Office hours: | + | * Office hours: -. |
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=== Course Description === | === Course Description === | ||
Introduction to fundamental ideas and techniques of stochastic analysis and modeling. Random variables, expectation and conditional expectation, joint distributions, covariance, moment generating function, central limit theorem, weak and strong laws of large numbers, discrete time stochastic processes, stationarity, power spectral densities and the Wiener-Khinchine theorem, Gaussian processes, Poisson processes, Brownian motion. The course develops applications in selected areas such as signal processing (Wiener filter), information theory, genetics, queuing and waiting line theory, and finance. | Introduction to fundamental ideas and techniques of stochastic analysis and modeling. Random variables, expectation and conditional expectation, joint distributions, covariance, moment generating function, central limit theorem, weak and strong laws of large numbers, discrete time stochastic processes, stationarity, power spectral densities and the Wiener-Khinchine theorem, Gaussian processes, Poisson processes, Brownian motion. The course develops applications in selected areas such as signal processing (Wiener filter), information theory, genetics, queuing and waiting line theory, and finance. | ||
Revision as of 15:55, 28 January 2019
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Introduction to Computational Electromagnetics | |
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Instructors
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Teaching Assistants
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Course Description
Introduction to fundamental ideas and techniques of stochastic analysis and modeling. Random variables, expectation and conditional expectation, joint distributions, covariance, moment generating function, central limit theorem, weak and strong laws of large numbers, discrete time stochastic processes, stationarity, power spectral densities and the Wiener-Khinchine theorem, Gaussian processes, Poisson processes, Brownian motion. The course develops applications in selected areas such as signal processing (Wiener filter), information theory, genetics, queuing and waiting line theory, and finance.
