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Delves into advanced probability theory, covering inequalities, trials, distributions, and calculations for probabilities and expectations.
Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.
Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.
