aspire: Accelerated Sequential Posterior Inference via REuse

tests

aspire is a framework for reusing existing posterior samples to obtain new results at a reduced cost.

Installation

aspire can be installed from PyPI using pip. By default, you need to install one of the backends for the normalizing flows, either torch or jax. We also recommend installing minipcn if using the smc sampler:

Torch

We recommend installing torch manually to ensure correct CPU/CUDA versions are installed. See the PyTorch installation instructions for more details.

pip install aspire-inference[torch,minipcn]

Jax:

We recommend install jax manually to ensure the correct GPU/CUDA versions are installed. See the jax documentation for details

pip install aspire-inference[jax,minipcn]

Important: the name of aspire on PyPI is aspire-inference but once installed the package can be imported and used as aspire.

Quickstart

import numpy as np
from aspire import Aspire, Samples

# Define a log-likelihood and log-prior
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)

# Create the initial samples
init = Samples(np.random.normal(size=(2_000, 4)))

# Define the aspire object
aspire = Aspire(
    log_likelihood=log_likelihood,
    log_prior=log_prior,
    dims=4,
    parameters=[f"x{i}" for i in range(4)],
)

# Fit the normalizing flow
aspire.fit(init, n_epochs=20)

# Sample the posterior
posterior = aspire.sample_posterior(
    sampler="smc",
    n_samples=500,
    sampler_kwargs=dict(n_steps=100),
)

# Plot the posterior distribution
posterior.plot_corner()

Documentation

See the documentation on ReadTheDocs.

Citation

If you use aspire in your work please cite the DOI and paper.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aspire_inference-0.1.0a21.tar.gz (88.9 kB view details)

Uploaded Jul 13, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

aspire_inference-0.1.0a21-py3-none-any.whl (68.9 kB view details)

Uploaded Jul 13, 2026 Python 3

File details

Details for the file aspire_inference-0.1.0a21.tar.gz.

File metadata

Download URL: aspire_inference-0.1.0a21.tar.gz
Upload date: Jul 13, 2026
Size: 88.9 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for aspire_inference-0.1.0a21.tar.gz
Algorithm	Hash digest
SHA256	`f7c7a6a40d73585da7c54a6f0627a0198d0c6e56f892f3f3836fc06c51f0af52`
MD5	`84967a3388c1a3c3d7638df9157b8d73`
BLAKE2b-256	`bdae31688e95563df187954da1ca7e131e3df391cdbd58be9b4793cd1a3a30df`

See more details on using hashes here.

Provenance

The following attestation bundles were made for aspire_inference-0.1.0a21.tar.gz:

Publisher: publish.yml on mj-will/aspire

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: aspire_inference-0.1.0a21.tar.gz
- Subject digest: f7c7a6a40d73585da7c54a6f0627a0198d0c6e56f892f3f3836fc06c51f0af52
- Sigstore transparency entry: 2162256252
- Sigstore integration time: Jul 13, 2026
Source repository:
- Permalink: mj-will/aspire@d6f3396da84c5ccfe83d3e231f31009cbe85a2b1
- Branch / Tag: refs/tags/v0.1.0a21
- Owner: https://github.com/mj-will
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish.yml@d6f3396da84c5ccfe83d3e231f31009cbe85a2b1
- Trigger Event: release