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 ParticleState and pass the returned metadata
to runner.run(..., metadata=metadata).
Standard Bayesian SMC#
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.
This is the helper used in the quick start. See Configuring an SMC runner for configuration and manual pipeline assembly.
Persistent Bayesian SMC#
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#
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
Working with results for the corresponding result interface.
All three helpers expose mutation, resampling, and particle-transform options; see Sampling options and their API references for supported settings. For standalone MCMC, see Standalone MCMC.