Estimation: Mean-Squared Error and Fisher Information

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Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Introduces Fisher Information for parameter estimation based on collected data.

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

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

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

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

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