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https://github.com/nchopin/particles
Sequential Monte Carlo in python
https://github.com/nchopin/particles
bayesian-inference kalman-filter particle-filter pmcmc quasi-monte-carlo sequential-monte-carlo smc2
Last synced: 2 months ago
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Sequential Monte Carlo in python
- Host: GitHub
- URL: https://github.com/nchopin/particles
- Owner: nchopin
- License: mit
- Created: 2018-11-16T16:03:47.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-10-14T14:21:02.000Z (3 months ago)
- Last Synced: 2024-10-29T22:54:01.253Z (3 months ago)
- Topics: bayesian-inference, kalman-filter, particle-filter, pmcmc, quasi-monte-carlo, sequential-monte-carlo, smc2
- Language: Python
- Size: 5.19 MB
- Stars: 409
- Watchers: 15
- Forks: 75
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- Contributing: CONTRIBUTING.md
- License: LICENSE
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- awesome-sciml - nchopin/particles: Sequential Monte Carlo in python
README
# particles #
Sequential Monte Carlo in python.
## Motivation ##
This package was developed to complement the following book:
[An introduction to Sequential Monte Carlo](https://www.springer.com/gp/book/9783030478445)
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.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_{t-1}
return dists.Normal(loc=xp) # X_t ~ N( X_{t-1}, 1)
def PY(self, t, xp, x): # Distribution of Y_t given X_t (and X_{t-1})
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:
```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](https://particles-sequential-monte-carlo-in-python.readthedocs.io/en/latest/)
for more examples, explanations, and installation instructions. (It is strongly
recommended to start with the
[tutorials](https://particles-sequential-monte-carlo-in-python.readthedocs.io/en/latest/tutorials.html),
to get a general idea on what you can do, and how.)
## Who do I talk to? ##
Nicolas Chopin ([email protected]) 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.