Stochastic Processes: Basics & Stationarity

Related lectures (30)

Page 2 of 3

Covers random variables, probability density functions, Gaussian distribution, and correlation in statistics.

Explores correlation limitations, probability, random variables, and statistical inference using a ball-drawing example.

Covers spectral densities, signal correlations, and white noise processes in stochastic systems.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Covers geostatistics, focusing on spatial data types and their analysis methods.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

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