Probability Random Variables And Stochastic Processes 4th Ed Athanasios Papoulis
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HW 10 assigned due 12/04/1312/4Mean square estimation, Interpolation, Markov sequences, Wiener Filter, Wiener Hopf equation, PredictorsPages 580-605HW 10 due12/11Approximately 1/3 of the final exam will cover material after exam 2 and 2/3 of the final exam will cover material up to and including exam 2. Three 8.5 x 11 Crib sheets may be used by students; this exam is closed book and notes. The final exam date is December 18, 2013 more details to come.Course ReviewCourse OutlineContinuous and discrete random variables and their joint Probability Distribution and density functions; Functions of one Random Variable and their distributions Independent Random Variables and conditional distributions; One Function of One and Two Random Variables and Two Functions of Two Random variables and their joint density functions; Jointly distributed Discrete Random Variables and their Functions; Characteristic Functions and Higher Order Moments; Covariance, Correlation, Orthogonality; Jointly Gaussian Random Variables; Linear functions of Gaussian Random Variables and their joint density functions. Stochastic Processes and the concept of Stationarity; Strict Sense Stationary (SSS) and Wide Sense Stationary (WSS) Processes; Auto correlation function and its properties; Poisson Processes and Wiener processes; Stochastic Inputs to Linear Time-Invariant (LTI) Systems and their input-output Autocorrelations; Input-Output Power Spectrum for Linear Systems with Stochastic Inputs; Minimum Mean Square Error Estimation(MMSE) and Orthogonality Principle; Auto Regressive Moving Average (ARMA) Processes and their power spectra. 2b1af7f3a8