Linked from

The 15 pages that link to Bayesian decision theory, each with the reason it gives.

• Precautionary principleCompared with: It makes probability-weighted trade-offs explicit, while precaution may act without reliable probabilities.

• Decision theoryBroader topic: It makes explicit how beliefs and values jointly determine an action.

• Statistical powerCompared with: It evaluates decisions and losses directly rather than defining performance through null-hypothesis rejection.

• Signal detection theoryRelated: It explains how priors and error costs can set an observer’s decision criterion.

• Expected utilityRelated: It uses expected utility to select actions under uncertainty.

• Bayesian statisticsRelated: Posterior probabilities guide actions when errors have unequal costs.

• Prior probabilityRelated: Prior beliefs can affect decisions when evidence is incomplete and outcomes have unequal costs.

• Bayesian probabilityRelated: It converts quantified uncertainty into choices under specified goals.

• Bayesian epistemologyRelated: It extends degrees of belief into practical choice under uncertainty.

• Information economicsRelated: It formalizes how decision-makers update beliefs when they acquire information.

• Posterior probabilityRelated: Decisions can use posterior probabilities to evaluate expected losses or benefits.

• Credible intervalRelated: Intervals can communicate uncertainty relevant to decisions, though decisions depend on losses too.

• Frank P. RamseyRelated: Ramsey’s account of belief and preference became a foundation for this framework.

• Sample size determinationRelated: It offers decision-focused alternatives when fixed power thresholds do not capture study goals.

• Detection theoryRelated: It supplies a broader decision framework when detection errors have unequal consequences.