Discrete Probability: Classical Theory and Examples

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Delves into probability, statistics, random experiments, and statistical inference, with practical examples and insights.

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores the short history of probability and basic concepts like sample space and event.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Covers properties and calculations related to the normal distribution, including probabilities and quantiles.

Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.

Introduces probability distributions, uniform distribution, probabilities of events, complements, unions, and disjoint events.

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