aspire: Accelerated Sequential Posterior Inference via REuse

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aspire: Accelerated Sequential Posterior Inference via REuse#

aspire is a lightweight framework for reusing existing posterior samples and normalizing flows to accelerate Bayesian inference. It focuses on practical workflows: fit a flow, adaptively run Sequential Monte Carlo (SMC), MCMC or importance samplers, and visualise or export the resulting samples.

Key capabilities#

Fit flow-based proposals (PyTorch or JAX backends) with automatic handling of bounded and periodic parameters.
Run adaptive SMC (MiniPCN or BlackJAX kernels) and importance sampling with detailed diagnostic histories.
Inspect results via convenience helpers for evidence estimates, corner plots, and HDF5/JSON export.

Quick start#

import numpy as np
from aspire import Aspire, Samples

def log_likelihood(samples):
    x = samples.x
    return -0.5 * np.sum(x**2, axis=-1)

def log_prior(samples):
    return -0.5 * np.sum(samples.x**2, axis=-1)

init = Samples(np.random.normal(size=(2_000, 4)))

aspire = Aspire(
    log_likelihood=log_likelihood,
    log_prior=log_prior,
    dims=4,
    parameters=[f"x{i}" for i in range(4)],
)
aspire.fit(init, n_epochs=20)
posterior = aspire.sample_posterior(
    sampler="smc",
    n_samples=500,
    sampler_kwargs=dict(n_steps=100),
)

posterior.plot_corner()

Use the sections below for environment setup, conceptual guidance, runnable examples, and the complete API reference.

Related Packages

aspire-bilby