Maximum Likelihood, MSE, Fisher Information, Cramér-Rao Bound

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Related lectures (32)

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Estimation and Confidence Intervals

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

Gaussian Processes: Designing Receivers

Covers the theory behind Gaussian processes and the design of receivers using MAP calculations.

Maximum Likelihood Estimation: Properties and Consistency

Explores Maximum Likelihood Estimation properties, consistency, and applications in statistical inference.

Statistical Models and Parameter Estimation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Estimators: Consistency and Efficiency

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Statistical Theory: Maximum Likelihood Estimation

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Statistical Estimators

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Optimality in Decision Theory: Unbiased Estimation

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

Estimators and Confidence Intervals

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

Mean-Square-Error Inference

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.