DOC: correct sampling method references in tutorials

docs/index.rst

@@ -51,6 +51,11 @@ explanations. Their
 run metadata identifies the source revision, parameters, seed, and pinned
 dependencies.
 
+When using these examples in research, follow the
+`software and algorithm citation guidelines
+<https://sequince.readthedocs.io/en/latest/citation.html>`_ for Sequince and
+any additional packages and methods used.
+
 For background and the full interface reference, see Sequince's
 `user guide <https://sequince.readthedocs.io/en/latest/user_guide.html>`_
 and `API reference <https://sequince.readthedocs.io/en/latest/api/index.html>`_.

examples/ns-smc/README.rst

@@ -3,6 +3,12 @@ NS-SMC
 
 Nested sampling via sequential Monte Carlo (NS-SMC) estimates evidence and
 posterior expectations by sampling through successive likelihood shells.
+The method is described by `Salomone et al. (2025)
+<https://doi.org/10.1093/jrsssb/qkaf015>`_ in *Unbiased and consistent nested
+sampling via sequential Monte Carlo*. The paper distinguishes fixed-threshold
+NS-SMC from adaptive NS-SMC (ANS-SMC). Sequince's helper uses adaptive
+thresholds by default; these do not inherit fixed-schedule unbiasedness
+guarantees.
 
 Start with the Gaussian example for basic usage. The spike-and-slab example
 compares nested and tempered SMC on a model with a phase transition.

examples/persistent_sampling/README.rst

@@ -4,7 +4,10 @@ Persistent sampling
 This example demonstrates how to use persistent sampling in ``sequince`` to
 sample an n-dimensional Rosenbrock target.
 
-Persistent sampling is a variation of SMC proposed by `Karamanis 2025`_.
+Persistent sampling is a variation of SMC proposed by
+`Karamanis and Seljak (2025)`_ in *Persistent sampling: enhancing the efficiency
+of sequential Monte Carlo*. It retains and reweights populations from previous
+stages.
 
 Run the example from the repository root:
 
@@ -43,7 +46,9 @@ Preconditioning
 
 First read :doc:`../preconditioning/README` for an explicit flow-transform
 setup. This example also shows how to use the normalizing-flow-based preconditioning
-described in `Karamanis 2022`_ and `Karamanis 2025`_.
+described in `Karamanis et al. (2022)`_, *Accelerating astronomical and
+cosmological inference with Preconditioned Monte Carlo*, combined with
+persistent sampling from `Karamanis and Seljak (2025)`_.
 
 The ``--flow`` option enables learned normalizing-flow preconditioning:
 
@@ -54,8 +59,8 @@ The ``--flow`` option enables learned normalizing-flow preconditioning:
 
 The flow option requires ``sequince-density`` and its flow dependencies.
 
-.. _Karamanis 2022: https://arxiv.org/abs/2207.05660
-.. _Karamanis 2025: https://arxiv.org/abs/2407.20722
+.. _Karamanis et al. (2022): https://doi.org/10.1093/mnras/stac2272
+.. _Karamanis and Seljak (2025): https://doi.org/10.1007/s11222-025-10682-y
 
 Example plots
 -------------

examples/preconditioning/README.rst

@@ -74,4 +74,4 @@ Example plots
    :language: python
    :linenos:
 
-.. _Karamanis et al. (2022): https://arxiv.org/abs/2207.05652
+.. _Karamanis et al. (2022): https://doi.org/10.1093/mnras/stac2272

examples/waste_free_smc/README.rst

@@ -2,7 +2,8 @@ Waste-free sequential Monte Carlo
 =================================
 
 :download:`run_waste_free_smc.py <run_waste_free_smc.py>` demonstrates waste-free
-sequential Monte Carlo (SMC), as described by `Dau & Chopin`_, alongside a
+sequential Monte Carlo (SMC), as described by `Dau and Chopin (2022)`_
+in *Waste-free sequential Monte Carlo*, alongside a
 standard resample-move SMC run. Both sample a four-dimensional correlated
 Gaussian likelihood with a uniform prior on ``[-10, 10]^4``.
 
@@ -82,4 +83,4 @@ The target densities and complete sampler setup are shown below:
    :language: python
    :linenos:
 
-.. _Dau & Chopin: https://arxiv.org/abs/2011.02328
+.. _Dau and Chopin (2022): https://doi.org/10.1111/rssb.12475