Statistical Estimation Methods

Related lectures (29)

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

Covers the theory of sampling, focusing on statistics for mathematicians.

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Covers the properties of the exponential family and the estimation of parameters.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

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

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

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.