feat!: name the unknown key, expose tail probabilities, flag short fits

Three issues from two downstream consumers, all small, all sharing a
theme: the crate had the information and would not hand it over.

#44 — `UnknownKey { team: 0, member: 0 }` did not say which key. A
consumer upgrading 0.1.2 -> 0.4.1 had every one of 5591 predictions
return this error, fell back to a neutral 0.5, and lost its entire
metadata model for a day. Nothing crashed and nothing logged; it was
found by sweeping an unrelated parameter and noticing the output did not
move. The 0.4.0 change that made unknown keys an error was right — the
error was just too anonymous to act on. It now carries the key's `Debug`
rendering, and its `Display` says what to do about it. The precondition
is documented on every prediction entry point, which the reporter said
would alone have saved the day.

#43 — `cdf` was `pub(crate)`, so a consumer asking "is this competitor
below the cutoff" approximated it with a `mu + z * sigma` band and had no
way to say what confidence any `z` bought. Adds
`Gaussian::probability_below` / `probability_above`. The second is
separate on purpose: `1 - cdf` collapses to exactly zero past ~8.3
sigma, and a stopping rule is evaluated precisely there. Both route
through the survival function added in 0.4.1, so this is visibility
rather than new numerics.

#50 — `ConvergenceReport` was not `#[must_use]`, so the one signal that
a fit stopped short was trivially discarded. It now is, and that
immediately found 78 sites doing exactly that — including this crate's
own ATP example, which was capped at 10 sweeps when the history needs
30. The example now reads the report and says so.

`ITERATIONS = 30` is documented as the floor it is, with the three
measurements to hand: 400 events over 100 competitors already stops
there at ~7e-3 against a 1e-6 tolerance, the ATP example needs 30 at a
much looser one, and a consumer's 2000-node model needs 76 to 161.

BREAKING CHANGE: `InferenceError::UnknownKey` gains a `key` field, and
the prediction methods now require `K: Debug` in order to fill it.

Closes #43, #50. Refs #44 — its third ask, an opt-in `UnknownKeys::Skip`
mode, is a live API question and deliberately not answered here.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ

tests/filtered.rs

@@ -47,7 +47,7 @@ fn tight() -> ConvergenceOptions {
 fn filtered_evidence_sits_between_coin_flip_and_batch() {
     let mut history = repeated_winner(5);
 
-    history.converge().unwrap();
+    let _ = history.converge().unwrap();
 
     let coin_flip = 5.0 * 0.5f64.ln();
     let batch = history.log_evidence();
@@ -71,7 +71,7 @@ fn filtered_evidence_sits_between_coin_flip_and_batch() {
 fn filtered_first_point_is_less_certain_than_smoothed() {
     let mut history = repeated_winner(12);
 
-    history.converge().unwrap();
+    let _ = history.converge().unwrap();
 
     let smoothed = history.learning_curve("a");
     let filtered = history.filtered_learning_curve("a");
@@ -121,7 +121,7 @@ fn filtered_first_point_is_less_certain_than_smoothed() {
 fn filtered_curves_plural_agrees_with_singular() {
     let mut history = repeated_winner(4);
 
-    history.converge().unwrap();
+    let _ = history.converge().unwrap();
 
     let curves = history.filtered_learning_curves();
 
@@ -180,7 +180,7 @@ fn single_slice_filtered_matches_smoothed() {
         ])
         .unwrap();
 
-    history.converge().unwrap();
+    let _ = history.converge().unwrap();
 
     let smoothed = history.learning_curve("a");
     let filtered = history.filtered_learning_curve("a");
@@ -223,13 +223,13 @@ fn filtered_curves_do_not_depend_on_ingestion_order() {
 
     let mut batched = History::builder().convergence(tight()).build();
     batched.add_events(all.clone()).unwrap();
-    batched.converge().unwrap();
+    let _ = batched.converge().unwrap();
 
     let mut incremental = History::builder().convergence(tight()).build();
     for event in all {
         incremental.add_events([event]).unwrap();
     }
-    incremental.converge().unwrap();
+    let _ = incremental.converge().unwrap();
 
     let from_batched = batched.filtered_learning_curve("a");
     let from_incremental = incremental.filtered_learning_curve("a");