Stochastic Models for Communications: Continuous-Time Stochastic Processes - Stationarity

Related lectures (31)

Page 3 of 4

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Covers exploratory statistics, focusing on populations, samples, and various statistical measures.

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Covers variational inference and neural networks for classification tasks.

Explains how to calculate average growth rates using arithmetic mean, geometric mean, and linear trend analysis.

Explores the calculation of growth rates, average GDP growth, and environmental footprint changes.

Covers ECDF, quantiles, mean, median, and box plots for dataset analysis.

Explores the calculation of weighted averages and interest rates, emphasizing the importance of averages in financial analysis.

Explores long memory in time series and Autoregressive Conditional Heteroskedasticity processes in financial data.

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