feat: add filtered learning curves

learning_curve returns post-convergence posteriors, so every point is
smoothed: the estimate at a given date incorporates rounds played years
later. On ustat's data that starts six players' curves already spread apart
at sigma 0.9-1.6 against a prior of 6.0, barely moving thereafter.

filtered_learning_curve plots the same competitor on forward-only
information, so everyone starts at the prior and fans out. It could not be
reconstructed from the public API before: a caller could only refit over
events[0..k] for every k, which is O(n^2) fits for something one forward
pass already computes.

src/history.rs

@@ -307,9 +307,6 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Learning curves for all competitors, keyed by their user-facing key.
-    ///
-    /// Note: `key(idx)` is O(n) per lookup; this method is therefore O(n²)
-    /// in the number of competitors. Acceptable for T2; T3 may optimize.
     pub fn learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
         #[cfg(feature = "rayon")]
         {
@@ -380,6 +377,53 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             .collect()
     }
 
+    /// Filtered learning curves for all competitors, keyed by user-facing key.
+    ///
+    /// Each point is the posterior using only events up to and including that
+    /// time — "what we knew then". Contrast `learning_curves`, whose points
+    /// are smoothed and so incorporate rounds played later.
+    ///
+    /// Runs a full forward pass per call and caches nothing.
+    pub fn filtered_learning_curves(&self) -> HashMap<K, Vec<(T, Gaussian)>> {
+        let mut data: HashMap<K, Vec<(T, Gaussian)>> = HashMap::new();
+
+        for (time, step) in self.filtered_pass() {
+            for (agent, posterior) in step.posteriors {
+                if let Some(key) = self.keys.key(agent).cloned() {
+                    data.entry(key).or_default().push((time, posterior));
+                }
+            }
+        }
+
+        data
+    }
+
+    /// Filtered learning curve for a single key: (time, posterior) pairs in
+    /// time order.
+    ///
+    /// Runs the same full pass as `filtered_learning_curves` and keeps one
+    /// key, so asking for several keys individually costs a pass each — use
+    /// the plural form for that.
+    pub fn filtered_learning_curve<Q>(&self, key: &Q) -> Vec<(T, Gaussian)>
+    where
+        K: Borrow<Q>,
+        Q: Hash + Eq + ?Sized,
+    {
+        let Some(idx) = self.keys.get(key) else {
+            return Vec::new();
+        };
+
+        self.filtered_pass()
+            .into_iter()
+            .filter_map(|(time, step)| {
+                step.posteriors
+                    .iter()
+                    .find(|(agent, _)| *agent == idx)
+                    .map(|&(_, posterior)| (time, posterior))
+            })
+            .collect()
+    }
+
     pub(crate) fn log_evidence_internal(&mut self, forward: bool, targets: &[Index]) -> f64 {
         #[cfg(feature = "rayon")]
         {

tests/filtered.rs

@@ -50,3 +50,68 @@ fn filtered_evidence_sits_between_coin_flip_and_batch() {
          scores each game on strictly less information than smoothing does"
     );
 }
+
+#[test]
+fn filtered_first_point_is_less_certain_than_smoothed() {
+    let mut history = repeated_winner(12);
+
+    history.converge().unwrap();
+
+    let smoothed = history.learning_curve("a");
+    let filtered = history.filtered_learning_curve("a");
+
+    assert_eq!(
+        smoothed.len(),
+        filtered.len(),
+        "both curves must cover the same time points"
+    );
+
+    let (smoothed_time, first_smoothed) = smoothed[0];
+    let (filtered_time, first_filtered) = filtered[0];
+
+    assert_eq!(smoothed_time, filtered_time);
+
+    assert!(
+        first_filtered.sigma() > first_smoothed.sigma(),
+        "filtered sigma {} at the first point is not above smoothed {}; the smoother \
+         collapses uncertainty before the first round is drawn, which is the whole \
+         reason this method exists",
+        first_filtered.sigma(),
+        first_smoothed.sigma()
+    );
+
+    assert!(
+        first_filtered.sigma() < trueskill_tt::SIGMA,
+        "filtered sigma {} at the first point is not below the prior {}; one game was \
+         played, so some uncertainty must have been resolved",
+        first_filtered.sigma(),
+        trueskill_tt::SIGMA
+    );
+
+    for pair in filtered.windows(2) {
+        assert!(
+            pair[1].1.mu() > pair[0].1.mu(),
+            "filtered mu must climb at every step for a competitor who wins every \
+             game: t={} mu={} then t={} mu={}",
+            pair[0].0,
+            pair[0].1.mu(),
+            pair[1].0,
+            pair[1].1.mu()
+        );
+    }
+}
+
+#[test]
+fn filtered_curves_plural_agrees_with_singular() {
+    let mut history = repeated_winner(4);
+
+    history.converge().unwrap();
+
+    let curves = history.filtered_learning_curves();
+
+    assert_eq!(
+        curves["b"],
+        history.filtered_learning_curve("b"),
+        "the plural form must agree with the singular for the same key"
+    );
+}