Lectures related to Admissible decision rule

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.

Decision Theory: Risk and Inference

Explores decision theory, risk functions, and inference in statistical analysis.

Sexing Guppy Fish: Bayesian Inference and Decision Rules

Covers the sexing of guppy fish using Bayesian inference and decision rules.

Discriminant Analysis: Bayes Rule

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

Densities and Bayesian Inference: Decision Rules and Bayes Law

Covers classification concepts, medical screening tests, and decision rules.

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Probabilistic Linear Regression

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

Bayesian Decision Theory: Utility, Risk, and Classification

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

Detection & Estimation

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Bayesian Inference: Precision in Gaussian Model

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

Ingredients of choice theory

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

Choice Theory: Theoretical Foundations

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

Decision Rules: Maximum a Posteriori Decision

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

Generative Learning Algorithms

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

Naive Bayes: Gaussian Discriminant Analysis

Covers the Naive Bayes algorithm with Gaussian Discriminant Analysis.

Image Processing II: Bayesian Classification and Decision Making

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

Reinforcement Learning: Markov Processes and Policy Optimization

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

Bayes Estimator: Definition and Application

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

Hypothesis Testing: A Different Perspective

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