Pre-built samplers ================== ``sequince`` provides three helpers for assembling Bayesian SMC pipelines. Each returns a runner, its target, and initial metadata. Supply your own initial :class:`~sequince.state.ParticleState` and pass the returned metadata to ``runner.run(..., metadata=metadata)``. Standard Bayesian SMC --------------------- :func:`~sequince.prebuilt_samplers.make_standard_bayesian_smc` moves a particle population through a sequence of tempered distributions, combining weighting, resampling, and mutation. It uses adaptive temperatures by default; ``adaptive=False`` with ``num_steps`` selects a fixed temperature schedule. The usual posterior endpoint is ``max_beta=1.0``. For the SMC sampler framework, see `Del Moral, Doucet, and Jasra (2006) `_. This is the helper used in the :ref:`quick start `. See :doc:`smc_configuration` for configuration and manual pipeline assembly. Persistent Bayesian SMC ----------------------- Persistent sampling was introduced by `Karamanis and Seljak (2025) `_. :func:`~sequince.prebuilt_samplers.make_persistent_bayesian_smc` retains an archive of previous particle batches and reweights that archive as the temperature changes. ``n_particles`` sets the active batch size; ``target_ess`` and ``target_total_ess`` are absolute effective sample sizes of the archive. The initial population must contain a positive multiple of ``n_particles`` equally weighted draws from the prior, or from ``log_proposal`` when supplied. By default, the sampler finishes with an equally weighted active population. Set ``resample_at_end=False`` to return the complete weighted archive instead. Nested-sampling SMC ------------------- Nested sampling via SMC (NS-SMC) follows the framework of `Salomone et al. (2025) `_. The paper distinguishes fixed-threshold NS-SMC from adaptive NS-SMC (ANS-SMC). The helper uses adaptive thresholds by default; these do not inherit the fixed-schedule unbiasedness guarantee. :func:`~sequince.prebuilt_samplers.make_nested_sampling_smc` raises a likelihood threshold and explores the remaining constrained prior. It accumulates evidence over the discarded likelihood shells and the final live population. Initial particles must represent a normalized prior; arbitrary importance proposals are not supported. Use ``threshold_quantile`` or ``threshold_schedule`` to control the thresholds. ``evidence_tolerance`` controls an estimated remaining-evidence stopping criterion; it is not a guaranteed error bound. Read evidence and reconstruct posterior samples through ``result.as_nested_sampling_result()``. Posterior reconstruction requires a completed run and ``store_shell_particles=True`` (the default). See :doc:`results` for the corresponding result interface. All three helpers expose mutation, resampling, and particle-transform options; see :doc:`../sampling_options/index` and their API references for supported settings. For standalone MCMC, see :doc:`../advanced_workflows/mcmc_sampler`. Waste-free SMC -------------- Waste-free SMC, introduced by `Dau and Chopin (2022) `_, retains intermediate MCMC states within each stage. Assemble it in a custom pipeline with :class:`~sequince.steps.waste_free.WasteFreeResampleMove`; see the `worked tutorial `_. Persistent sampling instead reuses populations across stages. Full method references are collected in :doc:`../../citation`.