Stochastic Models for Communications

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Stochastic Models for Communications

Covers stochastic models for communications and the analysis of random processes in communication systems.

Stochastic Processes: Ergodicity

Covers the concept of ergodicity in continuous-time stochastic processes.

Elements of Statistics: Estimation & Distributions

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Structure of Measure-Preserving Systems

Covers the abstract structure of measure-preserving systems and aims to understand their classification and ergodic decompositions.

Air Pollution: Correlation Analysis

Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.

Stochastic Processes: Ergodicity

Covers the concept of ergodicity in continuous-time stochastic processes and the convergence of statistical properties over time.

Continuous-Time Stochastic Processes: Stationarity

Explores stationarity in continuous-time stochastic processes, focusing on autocorrelation and cross-correlation functions.

Stochastic Processes: Basics & Stationarity

Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Continuous-Time Stochastic Processes: Ergodicism Examples

Illustrates ergodicism in continuous-time stochastic processes through examples and calculations.