Acceptance-Rejection Methods: Advanced Techniques

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Statistical Inference: Confidence Intervals

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Statistical Models: Families and Transformations

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Continuous Random Variables

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Elements of Statistics: Exercises

Covers exercises on Bayes' Theorem, moment generating functions, photon counts, disease probabilities, and distribution properties.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Central Limit Theorem

Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.

Probabilities and Statistics: Key Theorems and Applications

Discusses key statistical concepts, including sampling dangers, inequalities, and the Central Limit Theorem, with practical examples and applications.