Stochastic Models for Communications

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Related lectures (31)

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Continuous-Time Stochastic Processes: Ergodicity Examples

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Efficiency of Sampling: Ergodicity and Autocorrelation Functions

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Introduction to Quantum Chaos

Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.

Stationarity in Stochastic Processes

Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Stochastic Processes: 2nd Order Analysis

Explores stochastic processes, stationarity, ergodicity, and Wiener filtering for image restoration.

Stochastic Processes: Second Order Analysis

Covers the analysis of second-order stochastic processes in communication systems and image restoration using the Wiener filter.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.