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Stochastic Models for Communications
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Related lectures (33)
Stochastic Processes: Ergodicity
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Covers the concept of ergodicity in continuous-time stochastic processes and the convergence of statistical properties over time.
Continuous-Time Stochastic Processes: Stationarity
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Explores stationarity in continuous-time stochastic processes, focusing on autocorrelation and cross-correlation functions.
Continuous-Time Stochastic Processes: Ergodicism Examples
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Illustrates ergodicism in continuous-time stochastic processes through examples and calculations.
Signals & Systems II: Complex Random Vectors and Stationarity
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Explores complex random vectors, stationarity, ergodicity, and the analysis of signals.
Stochastic Processes: Ergodicity
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Explores ergodicity in continuous-time stochastic processes and its properties as time approaches infinity.
Causal Systems & Transforms: Delay Operator Interpretation
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Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.
Stochastic Processes: Stationarity in Continuous Time - Part 2
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Discusses weak-sense stationarity in continuous-time stochastic processes and the calculation of autocorrelation and cross-correlation functions.
Continuous-Time Stochastic Processes: Ergodicism Examples
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Introduces continuous-time stochastic processes and provides examples illustrating ergodicism.
Linear Prediction and Estimation
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Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.
Continuous-Time Markov Chains: Reversible Chains
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Covers continuous-time Markov chains, focusing on reversible chains and their properties.
Point Processes: Convergence and Gaussian Processes
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Covers point processes, convergence criteria, Laplace functionals, Gaussian processes, covariance functions, and intrinsic stationarity.
Spin Angular Momentum
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Explores spin as angular momentum, its operators, commutation relations, and eigenstates in quantum mechanics.
Stochastic Models for Communications
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Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.
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