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Related lectures (31)

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Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Descriptive Statistics: Hypothesis Testing

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Acceptance-Rejection Methods: Advanced Techniques

Explores advanced Acceptance-Rejection methods, sampling from normal distribution, and multivariate random variable generation.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.

Large Deviations Principle: Cramer's Theorem

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.

Eigenstate Thermalization Hypothesis

Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.

Data Exploration: Normal Distribution

Explores data distribution strategies, normal modeling, and checking normality with graphical tools.

Central Limit Theorem: Illustration and Applications

Explores the Central Limit Theorem and its applications in statistical analysis.

Poisson Process Mapping

Explains how q = rw defines a Poisson process and its intensity.

Statistical Models: Families and Transformations

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