Gaussian mixture models

GMM support is provided via scikit-learn. Install it with:

python -m pip install "sequince-density[gmm]"

Warning

The GMM backend uses NumPy on the CPU. Fitting and density evaluation do not run natively on other array backends or GPUs; inputs from other backends are converted to NumPy for computation.

Fit a mixture to a sample population, then draw samples and evaluate their log densities:

import numpy as np

from sequince_density import GaussianMixtureProposal

particles = np.random.default_rng(42).normal(size=(2000, 2))
proposal = GaussianMixtureProposal(n_components=4).fit(particles, rng=42)
samples, log_q = proposal.sample_and_log_prob(256, rng=43)

Fitting expects particles shaped (n_particles, n_dimensions). Nonuniform weights are not supported; use an equal-weight population, such as particles after resampling.

GaussianMixtureProposal implements Sequince’s proposal interface directly. See Proposal distributions for using it as a fitted starting distribution for SMC.