Stochastic Models for Communications

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Related lectures (31)

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Elements of Statistics: Estimation & Distributions

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

Variance, Covariance, and Correlation

Explores variance, covariance, and correlation in statistics, essential for data analysis.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Signal Processing Fundamentals

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

Elements of Statistics: Probability, Distributions, and Estimation

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

Multivariate Normal Distribution: Correlation and Covariance

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

Probability and Stochastic Processes: Fundamentals and Applications

Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.

Copulas: Properties and Applications

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Covariance of Differenced Measurements

Explores the concept of covariance in differenced measurements and correlation between observations.

Joint Distributions

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.