docs: name the statistical identity — Bayesian detective, frequentist court

The two halves of the system run on opposite statistical philosophies, on
purpose, and until now the docs never said so. The optimizer is a Bayesian
detective: playbook prior, ledger evidence, policy ranking, explain
feedback — belief-updating that decides what to try next. The trust layer
is a frequentist court: equivalence gates, per-machine calibrated error
rates (A/A false-positive bound, injected-regression power bound), and a
CI-lower-bound accept rule with no prior anywhere on the accept path —
because its audience is a hostile reviewer who shares none of your priors.

The load-bearing edge is the return path: evidence flows from court to
detective (ledger rows + explain); authority never flows the other way.
Invariant, now stated in four places: priors steer where we look; they
never touch what we conclude. A wrong prior costs compute — each wasted
attempt honestly ledgered. A prior in the verdict would cost a false
claim, the one unrecoverable failure for a project whose product is trust.

- thesis.md: full section with the two-box diagram incl. the return edge,
  in docket order (equivalence before measurement); notes that estimated
  payoff is a belief and dollar figures are minted only by the mechanical
  report from accepted rows, and that even the learning layer bets on
  Wilson LOWER bounds — the detective is a pessimist too.
- README: the one-breath version, linking the full argument.
- SPEC §1: dated note mapping each half to its sections.
- CLAUDE.md: the mental model, one paragraph under Mission.

Framing credit: emerged from a design conversation on whether peltier is
"Bayesian engineering" — it is exactly half of one.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -15,6 +15,14 @@ deltas with the methodology attached** — a stopwatch on pinned hardware, a
 proof of equivalence, and an ROI figure whose confidence interval survives
 hostile review.
 
+The design in one breath: **the optimizer is a Bayesian detective; the
+trust layer is a frequentist court.** The detective holds priors, updates
+them from the ledger, and picks the next bet; the court runs calibrated
+procedures with measured error rates and no priors at all. Evidence flows
+back from court to detective — authority never does. Priors steer where we
+look; they never touch what we conclude.
+[The full argument](https://bdbrown4.github.io/peltier.io/thesis.html#the-detective-and-the-court).
+
 📖 **Full documentation: the [peltier.io book](https://bdbrown4.github.io/peltier.io/)**
 · Design spec: [SPEC.md](SPEC.md) · Charter: [CLAUDE.md](CLAUDE.md)