Decision Theory: Risk and Inference

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Decision Theory: Risk and Hypothesis Testing

Covers decision theory, risk functions, and hypothesis testing in statistical inference.

Statistical Theory: Decision Theory Framework

Explores the Decision Theory Framework in Statistical Theory, viewing statistics as a random game with key concepts like admissibility, minimax rules, and Bayes rules.

Statistical Theory: Cramér-Rao Bound & Hypothesis Testing

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

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Optimality in Decision Theory: Unbiased Estimation

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

Probabilistic Models for Linear Regression

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

Maximum Likelihood Estimation

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

Bayesian Decision Theory: Utility, Risk, and Classification

Covers Bayesian decision theory, cost functions, classification, and decision rules.

Linear Regression: Statistical Inference and Regularization

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

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.