Model Selection in Statistics

Related lectures (31)

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Covers broadcasting, operations, comparisons, and numpy constants like pi, e, and infinity.

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Covers the applications of densities and statistics in quantum science, focusing on binomial and Poisson distributions.

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores variance, covariance, and practical applications in statistics and probability.

Introduces random variables, probability density functions, and Gaussian distribution in statistics.

Explores probability theory, distribution of averages, and the central limit theorem.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.