Maximum a posteriori estimation

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Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Explores decision rules based on likelihood ratios and the maximum a posteriori decision.

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Introduces the Bayes estimator, explaining its definition, application in quadratic cost scenarios, and importance in probabilistic reasoning.

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Explores survival analysis, logistic regression challenges, and contingency tables in medical statistics.

Explores statistical estimation, regression models, and model selection in parametric models.

Explores the estimation of expectation for discrete random variables, emphasizing its importance in probability theory.

Covers the Mathematics of Data, focusing on models, estimators, and practical issues in data analysis.