Admissible decision rule

Related lectures (29)

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Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.

Covers the theoretical foundations of choice theory and its key components for decision making.

Covers the theoretical foundations of choice theory, decision makers, alternatives, attributes, decision rules, utility, and behavioral assumptions.

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

Explores generative learning algorithms, decision rules, and Gaussian distribution properties in machine learning.

Covers the Naive Bayes algorithm with Gaussian Discriminant Analysis.

Explores Bayesian classification, decision making, and pattern recognition applications in image processing.

Covers Markov processes, decision rules, and policy optimization techniques in reinforcement learning.

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

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.