smcx 1.0.0

Sequential Monte Carlo for Apple silicon, built on MLX

smcx

Sequential Monte Carlo in JAX: particle filters, adaptive tempered SMC, and SMC² with a small, flat API. Runs on CPU, CUDA, and TPU through stock JAX, and on Apple-silicon GPUs through the optional jax-mps backend.

smcx succeeds smcjax; all of its functionality was ported forward and extended.

Install

pip install smcx            # CPU / CUDA / TPU via your jax install
pip install "smcx[metal]"   # + jax-mps for Apple-silicon GPUs

What's in the box

• Filters: bootstrap_filter, guided_filter (general g·f/q proposal weights), auxiliary_filter (twisted potentials), and liu_west_filter (joint state–parameter, labeled approximate).
• Static targets: temper — adaptive tempered SMC with an ESS-bisection schedule and covariance-adapted random-walk moves.
• Parameter inference: smc2 — nested SMC² with vmapped inner filters and PMMH rejuvenation.
• Resampling: systematic, stratified, multinomial, and residual — one contract, log-domain weights throughout, float32-safe query grids.
• Diagnostics: ESS traces, quantile tail-ESS, Pareto-k reliability, CRPS, cumulative log score, Bayes factors, posterior-predictive sampling, and a one-call diagnose summary.
• store_history=False on every filter drops memory from O(T·N) to O(N) with a bit-identical evidence estimate.

Every sampler is validated against exact references — Kalman oracles for the filters, conjugate evidence for tempering, grid-integrated posteriors for SMC² — with Monte-Carlo-calibrated gates, not loose tolerances.

Quick start

import jax.numpy as jnp
import jax.random as jr

import smcx

# A 1-D linear-Gaussian state-space model.
A, Q, R = 0.9, 0.5, 0.3


def init(key, n):
    return jr.normal(key, (n, 1))


def transition(key, z):
    return A * z + jnp.sqrt(Q) * jr.normal(key, z.shape)


def log_observation(y, z):
    return -0.5 * (jnp.log(2 * jnp.pi * R) + (y[0] - z[0]) ** 2 / R)


post = smcx.bootstrap_filter(
    jr.key(0),
    init,
    transition,
    log_observation,
    emissions,
    num_particles=10_000,
)
post.marginal_loglik  # unbiased evidence estimate (log-domain)
smcx.diagnose(post)  # ESS / diversity / Pareto-k health summary

Callbacks are per-particle; smcx vmaps them internally. Everything takes an explicit PRNG key, and posteriors are NamedTuples — ordinary JAX pytrees.

Apple silicon

The [metal] extra runs the same code on M-series GPUs via jax-mps. Filter correctness on Metal is gate-verified in this repository's test suite (SMCX_TEST_PLATFORM=mps runs it on the GPU), and several of the performance fixes that make the backend fast for SMC-shaped workloads were contributed upstream from this project (jax-mps #215, #216, #220). Metal is float32-only; the suite runs float64 on CPU and float32 on Metal automatically.

Development

Requires Python 3.11+ and uv.

git clone https://github.com/michaelellis003/smcx.git
cd smcx
uv sync
uv run pre-commit install
uv run pre-commit install --hook-type commit-msg
uv run pre-commit install --hook-type pre-push

A Makefile covers common tasks:

make test        # lint + pytest
make lint        # ruff check, format check, license headers, ty
make format      # add license headers, ruff format, ruff fix
make docs        # build docs

Releases are automated: python-semantic-release reads conventional commits on merge to main, bumps the version, tags, and publishes.

Design notes

Decision records live in docs/adr/ and dated evidence in docs/research/ — including the benchmark study that shaped the architecture: a native-MLX implementation of this library was built, measured against jax-mps on equal terms, and retired when the measurements showed the compatibility path could be made equally fast with fixes contributed upstream (docs/research/2026-07-16-jax-mps-internals.md). The MLX implementation remains in git history at the mlx-final tag.

Acknowledgments

smcx's design draws on the SMC ecosystem: smcjax (the predecessor library), particles and Chopin & Papaspiliopoulos's An Introduction to Sequential Monte Carlo (the Feynman-Kac architecture), BlackJAX (the resampling contract), Dynamax (container conventions), TensorFlow Probability (criterion/trace hooks), and design lessons from PyMC, FilterPy, pfilter, pyfilter, Stone Soup, pomp, nimbleSMC, and ArviZ. See CITATION.cff for formal references and docs/research/licensing.md for the full provenance record.

License

Apache-2.0