particles

Sequential Monte Carlo in python

https://github.com/nchopin/particles

Science Score: 46.0%

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Links to: springer.com
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1 of 9 committers (11.1%) from academic institutions
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Low similarity (11.3%) to scientific vocabulary

Keywords

bayesian-inference kalman-filter particle-filter pmcmc quasi-monte-carlo sequential-monte-carlo smc2

Last synced: 6 months ago · JSON representation

Repository

Sequential Monte Carlo in python

Basic Info

Host: GitHub
Owner: nchopin
License: mit
Language: Python
Default Branch: master
Size: 5.17 MB

Statistics

Stars: 465
Watchers: 14
Forks: 79
Open Issues: 12
Releases: 4

Topics

bayesian-inference kalman-filter particle-filter pmcmc quasi-monte-carlo sequential-monte-carlo smc2

Created over 7 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License

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 state-space 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: fixed-lag smoothing, on-line smoothing, two-filter smoothing (O(N) and O(N^2) variants).
Exact filtering/smoothing algorithms: Kalman (for linear Gaussian models) and forward-backward recursions (for finite hidden Markov models).
Standard and waste-free 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 state-space 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 state-space model:

```python import particles import particles.statespacemodels as ssm import particles.distributions as dists

class ToySSM(ssm.StateSpaceModel): def PX0(self): # Distribution of X0 return dists.Normal() # X0 ~ N(0, 1) def PX(self, t, xp): # Distribution of Xt given X{t-1} return dists.Normal(loc=xp) # Xt ~ N( X{t-1}, 1) def PY(self, t, xp, x): # Distribution of Yt given Xt (and X{t-1}) return dists.Normal(loc=x, scale=self.sigma) # Yt ~ N(X_t, sigma^2) ```

You may now choose a particular model within this class, and simulate data from it:

python 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:

python 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. (It is strongly recommended to start with the tutorials, to get a general idea on what you can do, and how.)

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.

Owner

Name: Nicolas Chopin
Login: nchopin
Kind: user
Location: Paris
Company: ENSAE, Institut Polytechnique de Paris

Website: https://nchopin.github.io/
Repositories: 2
Profile: https://github.com/nchopin

Professor of Data Sciences at ENSAE, Paris

GitHub Events

Total

Issues event: 9
Watch event: 55
Issue comment event: 10
Push event: 5
Pull request event: 10
Fork event: 5

Last Year

Issues event: 9
Watch event: 55
Issue comment event: 10
Push event: 5
Pull request event: 10
Fork event: 5

Committers

Last synced: over 2 years ago

All Time

Total Commits: 317
Total Committers: 9
Avg Commits per committer: 35.222
Development Distribution Score (DDS): 0.139

Past Year

Commits: 96
Committers: 2
Avg Commits per committer: 48.0
Development Distribution Score (DDS): 0.24

Top Committers

Name	Email	Commits
Nicolas Chopin	n**n@e**r	273
Virgile Andreani	a**a@u**r	23
AdrienCorenflos	a**s@g**m	7
CHOPIN Nicolas	c**n@l**r	7
Dau Hai Dang	h**u@p**u	2
Max Cohen	m**i@p**e	2
Finn Catling	f**g@g**m	1
Jon Wiersma	g**n@j**g	1
Jacob Louis Hoover	p****m	1

Committer Domains (Top 20 + Academic)

jonw.org: 1 pm.me: 1 polytechnique.edu: 1 labostat2.ensae.fr: 1 ulminfo.fr: 1 ensae.fr: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 65
Total pull requests: 37
Average time to close issues: about 1 month
Average time to close pull requests: 15 days
Total issue authors: 32
Total pull request authors: 13
Average comments per issue: 2.57
Average comments per pull request: 1.14
Merged pull requests: 20
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 8
Pull requests: 8
Average time to close issues: 11 days
Average time to close pull requests: about 1 month
Issue authors: 7
Pull request authors: 3
Average comments per issue: 2.38
Average comments per pull request: 0.38
Merged pull requests: 2
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

nchopin (12)
hai-dang-dau (6)
AdrienCorenflos (6)
MaurusGubser (5)
MercuryBench (3)
vwiela (3)
jorgemcgomes (3)
diolegend (2)
CaesarMordred (1)
williamzhao1999 (1)
draktr (1)
glandfried (1)
finncatling (1)
lionfish0 (1)
SchroederAdrian (1)

Pull Request Authors

MercuryBench (14)
AdrienCorenflos (12)
Armavica (5)
G-Kossi (4)
SchroederAdrian (2)
finncatling (2)
afcarzero1 (1)
hai-dang-dau (1)
maxjcohen (1)
jonjonw (1)
sakira (1)
dependabot[bot] (1)
postylem (1)

Top Labels

Issue Labels

bug (7) enhancement (4)

Pull Request Labels

dependencies (1)

Packages

Total packages: 1
Total downloads:
- pypi 1,688 last-month

Total dependent packages: 1
Total dependent repositories: 5
Total versions: 4
Total maintainers: 1

pypi.org: particles

Sequential Monte Carlo in Python

Documentation: https://particles.readthedocs.io/
License: MIT License
Latest release: 0.4
published over 2 years ago

Versions: 4
Dependent Packages: 1
Dependent Repositories: 5
Downloads: 1,688 Last month

Rankings

Dependent packages count: 4.8%

Dependent repos count: 6.6%

Average: 7.9%

Downloads: 12.4%

Maintainers (1)

nchopin

Last synced: 6 months ago

Dependencies

pyproject.toml pypi

joblib *
numba *
numpy >= 1.18, < 2
scikit-learn *
scipy >= 1.7

particles

Science Score: 46.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

particles

Motivation

Features

Example

Who do I talk to?

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: particles

Rankings

Maintainers (1)

Dependencies