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https://github.com/simsurace/FeedbackParticleFilters.jl
A Julia package that provides (feedback) particle filters for nonlinear stochastic filtering and data assimilation problems
https://github.com/simsurace/FeedbackParticleFilters.jl
assimilation bayesian-inference conditional-probability diffusions feedback feedback-particle-filters filtering-algorithm numerical-algorithms particle-filter stochastic-processes
Last synced: 3 months ago
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A Julia package that provides (feedback) particle filters for nonlinear stochastic filtering and data assimilation problems
- Host: GitHub
- URL: https://github.com/simsurace/FeedbackParticleFilters.jl
- Owner: simsurace
- License: mit
- Created: 2019-05-25T15:02:05.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-17T00:16:04.000Z (over 1 year ago)
- Last Synced: 2024-07-09T21:58:55.252Z (4 months ago)
- Topics: assimilation, bayesian-inference, conditional-probability, diffusions, feedback, feedback-particle-filters, filtering-algorithm, numerical-algorithms, particle-filter, stochastic-processes
- Language: Julia
- Homepage:
- Size: 7.07 MB
- Stars: 12
- Watchers: 2
- Forks: 2
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-sciml - simsurace/FeedbackParticleFilters.jl: A Julia package that provides (feedback) particle filters for nonlinear stochastic filtering and data assimilation problems
README
# FeedbackParticleFilters.jl
[](http://simsurace.github.io/FeedbackParticleFilters.jl/dev)
[](https://github.com//simsurace/FeedbackParticleFilters.jl/actions/workflows/ci.yaml)
[](https://www.repostatus.org/#active)
[](https://codecov.io/gh/simsurace/FeedbackParticleFilters.jl)
This package's aim is to provide a versatile and efficient feedback particle filter implementation in Julia, with abstractions to flexibly construct, run, and analyze feedback particle filters for a variety of uni- and multivariate filtering problems with both diffusion and point process observations.
It provides implementations of the following algorithms:
- Kalman-Bucy filter `KBF`
- Feedback Particle Filter `FPF`
- Bootstrap Particle Filter (weighted) `BPF`
- Point-process Feedback Particle Filter `ppFPF`
- Ensemble Kushner-Stratonovich-Poisson Filter `EKSPF`
as well as
* Hidden state and observation models: diffusions, Poisson processes, etc.
* A variety of gain estimation methods: constant gain, semigroup, reproducing kernel Hilbert space, etc.
* Deterministic particle flow
If you have questions or comments, please open an issue!
## Installation
This package is officially registered.
To install it, use the built-in package manager:
```julia
pkg> add FeedbackParticleFilters
```
The package is currently tested on Julia 1.6-1.8, but should work on earlier versions too.
## Usage
To load the package, use the command:
```julia
using FeedbackParticleFilters
```
Set up a basic one-dimensional linear-Gaussian continuous-time filtering problem:
```julia
using Distributions
state_model = ScalarDiffusionStateModel(x->-x, x->sqrt(2.), Normal())
obs_model = ScalarDiffusionObservationModel(x->x)
filt_prob = FilteringProblem(state_model, obs_model)
```
Once the filtering problem is defined, an appropriate filtering algorithm can be defined like this:
```julia
method = ConstantGainApproximation()
filter = FPF(filt_prob, method, 100)
```
The package comes with methods to automatically simulate a given system:
```julia
simulation = ContinuousTimeSimulation(filt_prob, filter, 10000, 0.01)
run!(simulation)
```
To learn more about how to use this package, please check out some tutorials or the documentation linked below.
## Tutorials
There are various Jupyter notebooks that explore various key functions of the package:
1. [Getting started](https://github.com/simsurace/FeedbackParticleFilters.jl/blob/master/notebooks/Getting_started.ipynb)
2. Gain estimation using the [semigroup method](https://github.com/simsurace/FeedbackParticleFilters.jl/blob/master/notebooks/Gain_semigroup.ipynb)
3. [Harmonic oscillator example](https://github.com/simsurace/FeedbackParticleFilters.jl/blob/master/notebooks/Harmonic_oscillator.ipynb)
## Documentation
[In development...](https://simsurace.github.io/FeedbackParticleFilters.jl/dev)
## Acknowledgements
This package was developed as part of academic research at Department of Physiology, University of Bern, Switzerland.
The research was funded by the Swiss National Science Foundation.