Maximum Likelihood: Estimation and Inference

Related lectures (30)

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Introduces Fisher Information for parameter estimation based on collected data.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

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

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

Covers the fundamental concepts of statistics and their application in data science.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.