Admissible decision rule
In statistical decision theory, an admissible decision rule is a rule for making a decision such that there is no other rule that is always "better" than it (or at least sometimes better and never worse), in the precise sense of "better" defined below. This concept is analogous to Pareto efficiency.
| Part of a series on |
| Bayesian statistics |
|---|
| Posterior = Likelihood × Prior ÷ Evidence |
| Background |
| Model building |
| Posterior approximation |
| Estimators |
| Evidence approximation |
| Model evaluation |
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