Maximum Likelihood Estimation: Theory and Examples

Related lectures (31)

Covers exponential families and maximum entropy distributions under moment constraints.

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Explores completeness, minimal sufficiency, and special statistical models, focusing on exponential and transformation families.

Introduces Fisher Information for parameter estimation based on collected data.

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Covers the estimation of distributions using samples and probability models.

Covers regression with exponential family responses using Generalised Linear Models.

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

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.

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