Parameter Estimation & Fisher Information

Related lectures (32)

Page 1 of 4

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

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

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Introduces Fisher Information for parameter estimation based on collected data.

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

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

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

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

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