Lectures related to Stochastic Models for Communications

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.

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.