Sequential Monte Carlo in Python
Project description
particles
Sequential Monte Carlo in python.
Motivation
This package was developed to complement the following book:
An introduction to Sequential Monte Carlo
by Nicolas Chopin and Omiros Papaspiliopoulos.
It now also implements algorithms and methods introduced after the book was published, see below.
Features

particle filtering: bootstrap filter, guided filter, APF.

resampling: multinomial, residual, stratified, systematic and SSP.

possibility to define statespace models using some (basic) form of probabilistic programming; see below for an example.

SQMC (Sequential quasi Monte Carlo); routines for computing the Hilbert curve, and generating RQMC sequences.

FFBS (forward filtering backward sampling): standard, O(N^2) variant, and faster variants based on either MCMC, pure rejection, or the hybrid scheme; see Dau & Chopin (2022) for a discussion. The QMC version of Gerber and Chopin (2017, Bernoulli) is also implemented.

other smoothing algorithms: fixedlag smoothing, online smoothing, twofilter smoothing (O(N) and O(N^2) variants).

Exact filtering/smoothing algorithms: Kalman (for linear Gaussian models) and forwardbackward recursions (for finite hidden Markov models).

Standard and wastefree SMC samplers: SMC tempering, IBIS (a.k.a. data tempering). SMC samplers for binary words (Schäfer and Chopin, 2014), with application to variable selection.

Bayesian parameter inference for statespace models: PMCMC (PMMH, Particle Gibbs) and SMC^2.

Basic support for parallel computation (i.e. running multiple SMC algorithms on different CPU cores).

Variance estimators (Chan and Lai, 2013 ; Lee and Whiteley, 2018; Olsson and Douc, 2019).

nested sampling: both the vanilla version and the SMC sampler of Salomone et al (2018).
Example
Here is how you may define a parametric statespace model:
import particles
import particles.state_space_models as ssm
import particles.distributions as dists
class ToySSM(ssm.StateSpaceModel):
def PX0(self): # Distribution of X_0
return dists.Normal() # X_0 ~ N(0, 1)
def PX(self, t, xp): # Distribution of X_t given X_{t1}
return dists.Normal(loc=xp) # X_t ~ N( X_{t1}, 1)
def PY(self, t, xp, x): # Distribution of Y_t given X_t (and X_{t1})
return dists.Normal(loc=x, scale=self.sigma) # Y_t ~ N(X_t, sigma^2)
You may now choose a particular model within this class, and simulate data from it:
my_model = ToySSM(sigma=0.2)
x, y = my_model.simulate(200) # sample size is 200
To run a bootstrap particle filter for this model and data y
, simply do:
alg = particles.SMC(fk=ssm.Bootstrap(ssm=my_model, data=y), N=200)
alg.run()
That's it! Head to the documentation for more examples, explanations, and installation instructions.
Who do I talk to?
Nicolas Chopin (nicolas.chopin@ensae.fr) is the main author, contributor, and person to blame if things do not work as expected.
Bug reports, feature requests, questions, rants, etc are welcome, preferably on the github page.
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