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Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.
Delves into advanced probability theory, covering inequalities, trials, distributions, and calculations for probabilities and expectations.
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 Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
Explores modes of convergence in probability and statistics, illustrating concepts with examples and discussing the continuity theorem.
Covers advanced topics in probability theory, focusing on geometric distribution and variance calculations.
