62Cxx Decision theory
This subtopic introduces the core ideas in decision theory, including foundational concepts, standard methods, and the main questions used to organize the area. Typical uses include building mathematical background, framing related research problems, and supporting applications in neighboring fields where these concepts provide useful structure.
Specific topics
62C05 General considerations in statistical decision theory
Overview
General considerations in statistical decision theory. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C07 Complete class results in statistical decision theory
Overview
Complete class results in statistical decision theory. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C10 Bayesian problems; characterization of Bayes procedures
Overview
Bayesian problems; characterization of Bayes procedures. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C12 Empirical decision procedures; empirical Bayes procedures
Overview
Empirical decision procedures; empirical Bayes procedures. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C15 Admissibility in statistical decision theory
Overview
Admissibility in statistical decision theory. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C20 Minimax procedures
Overview
Minimax procedures. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks
62C25 Compound decision problems
Overview
Compound decision problems. This topic covers statistical decision theory, optimal rules, admissibility, minimax principles, and Bayesian decision formulations.
Related Wikipedia Page
Wikipedia: Decision theory
Useful Links
Key Ideas
- loss functions and risk
- optimality and admissibility
- minimax and Bayesian criteria
Typical Uses
Used to formalize and optimize statistical actions under uncertainty and competing criteria.
Applications
- Hypothesis testing design
- Sequential and adaptive decisions
- Machine learning decision frameworks
References
Recommended Textbooks