Estimation: Linear Estimator

Related lectures (32)

Page 2 of 4

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

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

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

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

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

Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

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

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.