sequince.steps.waste_free

Waste-free resampling and chain retention around an existing mutation.

Classes

WasteFreeResampleMove

Resample chain starts and retain a wrapped mutation's full chains.

Module Contents

class sequince.steps.waste_free.WasteFreeResampleMove(mutation, *, chain_length)

Bases: sequince.steps.base.SMCStep

Resample chain starts and retain a wrapped mutation’s full chains.

Implements the resampling and chain-retention portion of Algorithm 2 of Dau and Chopin [1]. With N particles and chain length P, resample M=N/P starts using the input weights, run P-1 transitions and keep all P states per chain. Starts and repeated states after rejection are retained. No further resampling occurs inside this step; the output has N uniform weights and preserves the input evidence estimate.

Parameters:

• mutation (sequince.steps.mutation.MutationStep) – A MutationStep supporting apply_with_n_steps() and stored chains, such as MetropolisHastingsMutation, BlackJAXMutation or MiniPCNMutation. It owns the target, proposal, transforms and transition logic. One complete block of P-1 steps is requested, overriding its default n_steps for this call only. Chain storage is temporarily enabled and its previous setting restored even if mutation fails.

• chain_length (int) – Number P of retained states per chain, INCLUDING the start. Must be a positive integer dividing N. P=1 only resamples; mutation is not called.

Examples

With 1000 input particles, run 20 chains with 50 retained states each:

>>> mutation = MetropolisHastingsMutation(target, GaussianRandomWalk(0.5))
>>> step = WasteFreeResampleMove(mutation, chain_length=50)

References

[1]

Dau, H.-D. and Chopin, N. (2022). Waste-free Sequential Monte Carlo. JRSS B, 84(1), 114-148. https://doi.org/10.1111/rssb.12475 Algorithm 2: https://arxiv.org/abs/2011.02328

mutation: sequince.steps.mutation.MutationStep

apply(state, context, rng)

Parameters:

• state (sequince.state.ParticleState)

• context (sequince.steps.base.StepContext)

• rng (orng.RandomGenerator)

Return type:

tuple[sequince.state.ParticleState, sequince.steps.base.StepContext, dict[str, Any]]

state_dict()

Forward the mutation’s checkpoint state.

Return type:

dict[str, Any]

load_state_dict(state)

Restore the wrapped mutation’s checkpoint state.

Parameters:

state (collections.abc.Mapping[str, Any])

Return type:

None