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Related lectures (31)

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Ensemble Fondamental: Events Space

Discusses the fundamental set and the events space with illustrative examples.

Estimating Moments: GEV and GPD

Explores moment estimation in GEV and GPD models, including L-moment estimation and robust parameter estimation.

Bayesian Extremes: Markov Chain Monte Carlo

Explores Bayesian techniques for extreme value problems, including Markov Chain Monte Carlo and Bayesian inference, emphasizing the importance of prior information and the use of graphs.

Estimating Expectation

Explores the estimation of expectation for discrete random variables, emphasizing its importance in probability theory.

Extreme Value Theory: Applications and Estimation

Explores Extreme Value Theory applications, estimation strategies, and modelling techniques for statistical analysis of extremes in time series.

Estimating R: Uniform, Hypergeometric, Poisson

Covers the estimation of R through balanced dice, uniform, hypergeometric, and Poisson distributions.

Probability Theory: Basics

Introduces the basics of probability theory, covering events, intersections, unions, and sample space.

Probability Laws: Random Variables

Covers the definition of probability laws for random variables and examples of discrete and continuous variables.

Combinatorial Principles: Factorials and Probabilities

Introduces combinatorial principles, factorials, probabilities, harmonic series convergence, and strategic decision-making in a romantic scenario.

Binary Response: Link Functions

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.