fix!: per-key queries report unknown keys instead of a plausible constant

Two accessors answered a question about a key the history had never seen
with a well-formed value indistinguishable from a real answer.

`log_evidence_for` filter_map'd unknown keys away. An empty target list
means "no restriction" downstream, so a list of *entirely* unknown keys
returned the whole-history evidence: measured on a two-cohort fixture,
`log_evidence_for(["typo"])` returned exactly `log_evidence()`. On the
one workload it is documented for — leave-one-out cross-validation —
that is the un-held-out score, a plausible number that silently
invalidates the comparison it was computed for. It now returns
`Err(UnknownKey)` naming the offending position.

`learning_curve` and `filtered_learning_curve` returned an empty `Vec`
both for a typo'd key and for a competitor who is registered but has not
played yet. They now return `Option`, so `None` is "never heard of it"
and `Some(vec![])` is "known, no appearances".

Tests carry a control case in each direction, so they cannot pass by
everything returning the same thing.

Closes #66, closes #70.

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

tests/degenerate_inputs.rs

@@ -184,7 +184,10 @@ fn event_builder_weights_mismatch_leaves_the_history_untouched() {
         .winner(0)
         .commit();
 
-    assert!(h.learning_curve("a").is_empty());
+    // The rejected event never reached the history, so "a" was never interned.
+    // `None` is the honest answer, and it is distinguishable from a competitor
+    // that IS known but has no appearances yet.
+    assert!(h.learning_curve("a").is_none());
 }
 
 #[test]
@@ -199,7 +202,7 @@ fn empty_event_stream_then_converge() {
 fn empty_history_queries_do_not_panic() {
     let h = History::default();
     assert!(h.learning_curves().is_empty());
-    assert!(h.learning_curve("nobody").is_empty());
+    assert!(h.learning_curve("nobody").is_none());
     assert!(h.current_skill("nobody").is_none());
 }
 
@@ -322,7 +325,7 @@ fn empty_history_has_no_filtered_estimates() {
 
     assert!(history.filtered_learning_curves().is_empty());
 
-    assert!(history.filtered_learning_curve("nobody").is_empty());
+    assert!(history.filtered_learning_curve("nobody").is_none());
 }
 
 // --- Boundary inputs (#26) ----------------------------------------------
@@ -337,7 +340,7 @@ fn tight() -> ConvergenceOptions {
 
 fn assert_curve_finite(h: &History, keys: &[&str], what: &str) {
     for key in keys {
-        for (time, g) in h.learning_curve(*key) {
+        for (time, g) in h.learning_curve(*key).unwrap() {
             assert!(
                 g.mu().is_finite() && g.sigma().is_finite(),
                 "{what}: non-finite posterior for {key} at t={time} (mu={} sigma={})",