This release is a pre-release and may not be stable for production use.
aspire: Accelerated Sequential Posterior Inference via REuse
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
pip install aspire-inference
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.
1 maintainer
Metadata
1 maintainer
Release files for aspire-inference 0.1.0a9
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