Stochastic Processes: Spectral Density, Markov Chains, and Gaussian Variables

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Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Explores discrete random variables, mass functions, and distribution functions in probability and statistics.

Discusses properties and functions of discrete random variables, including binomial and negative binomial distributions.

Explores moment-generating functions, linear combinations, and normality of random variables.

Explores Bayes' Theorem for defective parts detection, discrete random variables, and distribution functions, with practical examples and exercises.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Introduces probability mass functions for discrete random variables and various distributions, emphasizing the calculation of expectations.

Explores generating functions, Laplace transform, and their role in probability distributions.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.