Found 2 records with CourseId:9909 in academic section database. Picking the first.

Course Name: EE5110 : Probability Foundations for Signal Processing

Description: Various definitions of probability, axioms of probability, basic properties derived from the axioms, conditional probability, total probability, Bayes? rule, Independence of events, combined experiments and independence, binary communication channel example (MAP and ML decoding). Random variables: Definition, cumulative distribution function (cdf), continuous, discrete and mixed random variables, probability density function (pdf), examples of random variables, physical interpretation of pdf?s (histograms), multiple random variables, joint distribution ? definition and properties, joint density ? definition and properties, marginal distribution and density, conditional distribution and density, independence of random variables, expectations, moments, central moments, properties of expectation operator, mean, variance, Markov inequality, Chebyshev inequality, Chernoff bound, effect of linear transformations on mean and variance, autocorrelation, crosscorrelation, covariance, Cauchy-Schwartz inequality, condit

Slot: E

RoomNo: ESB 242

Instructor: Krishna Jagannathan

Period: JUL-NOV 2013

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