/**
 * CONFIDENCE — the probabilistic stop. FRONTIER says "looks like you can stop" from the diminishing-returns
 * curve; CONFIDENCE puts a real, CALIBRATED probability on it: "the chance one more experiment beats your
 * current best is ~p, so you can stop with (1−p) confidence."
 *
 * The estimate is distribution-free, built on RECORD STATISTICS. For exchangeable (iid) sampling the
 * probability that the next observation is a new maximum is exactly 1/(n+1) — independent of the
 * distribution, a beautiful fact. An optimiser is not iid: once it converges, new records stop coming, so we
 * deflate that baseline by how active records have been RECENTLY (records in a trailing window vs how many
 * iid would have produced). Still improving fast → more records than iid → the probability rises; long since
 * a record → it falls toward zero. The result is a probability you can actually trust, not a hunch.
 *
 * Honest by construction (DIAKRISIS): "probability the next experiment is a new best" — NOT a proof the
 * global optimum is found (a sharp peak can hide between samples; that's what the optimality certificate is
 * for). It is CALIBRATED on iid data (the gauntlet checks the predicted probability matches the observed
 * record frequency) and abstains when there's too little history.
 */
import { type Observation, type Goal } from "./engine.js";
export interface ConfidenceReport {
    n: number;
    stepsSinceRecord: number;
    records: number;
    pImprove: number;
    confidence: number;
    recommendation: "stop" | "continue" | "unknown";
    note: string;
}
/** Calibrated probability that one more experiment beats the current best, via record statistics. */
export declare function stopConfidence(obs: ReadonlyArray<Observation>, goal?: Goal): ConfidenceReport;
export declare function confidenceGauntlet(): {
    score: 0 | 100;
    checks: Array<{
        name: string;
        pass: boolean;
        detail: string;
    }>;
};
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