Getting started

Installation

The project is published from the main branch and can be installed directly from GitHub:

pip install git+https://github.com/sequince-dev/sequince.git

sequince targets Python 3.10+ and depends on numpy, scipy, matplotlib, and emcee. These will be installed automatically when the package is installed via pip.

Quick start

The library exposes a high-level helper, sequince.make_standard_bayesian_smc, that wires together a sensible set of steps for many Bayesian posterior inference workflows. The example below mirrors the usage demonstrated in the README

import numpy as np
from sequince import ParticleState, make_standard_bayesian_smc

# User-supplied log-density functions
def log_prior(theta):
    ...

def log_likelihood(theta):
    ...

smc, posterior, metadata = make_standard_bayesian_smc(
    log_prior,
    log_likelihood,
)

shared_context = dict()

# Build the initial particle cloud
n = 2_048
initial_particles = np.random.normal(size=(n, 2))
initial_state = ParticleState(
    particles=initial_particles,
    log_weights=np.full(n, -np.log(n)),
)

results = smc.run(
    initial_state,
    rng=np.random.default_rng(),
    shared_context=shared_context,
    metadata=metadata,
)
samples = results.posterior_samples(1_000)

Check the User guide for a deeper look at how to customise the SMC loop and swap in different building blocks.

Meta data & shared context

The metadata dictionary is intended to hold information that needs to be passed between steps in the SMC loop. It can be updated at each step.

The shared dictionary is intended to hold information that needs to be passed between steps in the SMC loop, but is not intended to be updated. For example, it can be used to store static data that is needed by the likelihood function.