improved doc

Author: AlexMoreo

CHANGE_LOG.txt

@@ -1,7 +1,7 @@
 Change Log 0.2.1
 -----------------
 
-...
+- Improved documentation of confidence regions.
 
 Change Log 0.2.0
 -----------------

docs/source/manuals/methods.md

@@ -604,7 +604,10 @@ estim_prevalence = model.predict(dataset.test.X)
 
 _(New in v0.2.0!)_ Some quantification methods go beyond providing a single point estimate of class prevalence values and also produce confidence regions, which characterize the uncertainty around the point estimate. In QuaPy, two such methods are currently implemented:
 
-* Aggregative Bootstrap: The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. Key features of this method include:
+* Aggregative Bootstrap: The Aggregative Bootstrap method extends any aggregative quantifier by generating confidence regions for class prevalence estimates through bootstrapping. The method is described in the paper [Moreo, A., Salvati, N.
+    An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
+    Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
+    pp 12-33, Porto (Portugal)](https://lq-2025.github.io/proceedings/CompleteVolume.pdf). Key features of this method include:
 
     * Optimized Computation: The bootstrap is applied to pre-classified instances, significantly speeding up training and inference.
 During training, bootstrap repetitions are performed only after training the classifier once. These repetitions are used to train multiple aggregation functions.

quapy/method/confidence.py

@@ -339,6 +339,12 @@ class AggregativeBootstrap(WithConfidenceABC, AggregativeQuantifier):
 
     During inference, the bootstrap repetitions are applied to the pre-classified test instances.
 
+    See
+    `Moreo, A., Salvati, N.
+    An Efficient Method for Deriving Confidence Intervals in Aggregative Quantification.
+    Learning to Quantify: Methods and Applications (LQ 2025), co-located at ECML-PKDD 2025.
+    pp 12-33 <https://lq-2025.github.io/proceedings/CompleteVolume.pdf>`_
+
     :param quantifier: an aggregative quantifier
     :para n_train_samples: int, the number of training resamplings (defaults to 1, set to > 1 to activate a
         model-based bootstrap approach)
@@ -437,7 +443,7 @@ def _classifier_method(self):
 
 class BayesianCC(AggregativeCrispQuantifier, WithConfidenceABC):
     """
-    `Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ method,
+    `Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ method (by Albert Ziegler and Paweł Czyż),
     which is a variant of :class:`ACC` that calculates the posterior probability distribution
     over the prevalence vectors, rather than providing a point estimate obtained
     by matrix inversion.