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Export to ArviZ

Install the optional dependency. The example below uses the result and observations from the quickstart.

pip install "smcx[arviz]"

import jax.random as jr
import smcx

result = smcx.to_arviz(
    posterior,
    key=jr.key(7),
    num_draws=1_000,
    emissions=observations,
)

posterior above can be a ParticleFilterPosterior or TemperedPosterior. Pass a sequence of independent results to represent multiple chains. num_draws controls how many equal-weight draws are resampled from each particle cloud; the key makes that resampling reproducible.

Groups

Group or attribute Contents
posterior Resampled particle values
particle_diagnostics Source log weights and algorithm diagnostics
observed_data Emissions, when supplied
unconstrained_posterior Aligned u-space values, when supplied
posterior.attrs["marginal_loglik"] Evidence estimate for each run

Particle-filter output has dimensions (chain, draw, time, ...). Each time slice is a filtering marginal \(p(x_t \mid y_{0:t})\); draws with the same index across time do not form a joint trajectory. Use reconstruct_trajectories when ancestry is needed.

The particle_diagnostics group describes the source particle clouds, not the resampled posterior draws. Its leading dimension is run, followed by time for particle filters or stage for tempered SMC; source log weights also have a particle dimension. These values are stored once per run rather than repeated across draw.

Structured particle states use their PyTree paths as variable names. Supply var_names to rename them and dims to label event dimensions. Values passed through unconstrained= follow the same resampling indices as the constrained particles.

ArviZ 0.x returns InferenceData; ArviZ 1.x returns DataTree. smcx dispatches to the constructor available in the installed generation: arviz.from_dict for 0.23.4 or arviz_base.from_dict for 1.x. ArviZ is distributed under the Apache License 2.0.