https://github.com/simsurace/stochasticfilters.jl

A Julia package that provides high-level abstractions for simulating and deploying stochastic filters
https://github.com/simsurace/stochasticfilters.jl

bayesian-filtering bayesian-inference continuous-time discrete-time filtering filtering-algorithm hidden-markov-model kalman-filter particle-filter state-space-model stochastic-processes

Last synced: about 1 month ago
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A Julia package that provides high-level abstractions for simulating and deploying stochastic filters

• Host: GitHub
• URL: https://github.com/simsurace/stochasticfilters.jl
• Owner: simsurace
• License: mit
• Created: 2021-05-08T11:47:59.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-10-31T16:26:52.000Z (about 2 years ago)
• Last Synced: 2024-10-13T22:34:44.847Z (3 months ago)
• Topics: bayesian-filtering, bayesian-inference, continuous-time, discrete-time, filtering, filtering-algorithm, hidden-markov-model, kalman-filter, particle-filter, state-space-model, stochastic-processes
• Language: Julia
• Homepage:
• Size: 21.5 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# StochasticFiltering

[![Build Status](https://travis-ci.com/simsurace/StochasticFiltering.jl.svg?branch=master)](https://travis-ci.com/simsurace/StochasticFiltering.jl)

[![Coverage](https://codecov.io/gh/simsurace/StochasticFiltering.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/simsurace/StochasticFiltering.jl)

The StochasticFiltering.jl package provides high-level abstractions for simulating and deploying stochastic filters. As such, it is primarily targeted at developers or researchers of specific stochastic filtering algorithms. It also comes with concrete types for the most common hidden state and observation models.

## Installation

Within Julia 1.6.0+, press `]` to use the built-in package manager, then type

```

pkg> add https://github.com/simsurace/StochasticFiltering.jl

```

## Usage

User API is in development.

Some planned features:

* Defining filtering problems as well as combining signal and observation models in various ways (adding new observations, stacking, etc.)

* Simulating hidden states, observations, and the filter in order to assess performance

* Deploying the filter as a standalone object

* Comparing different filters side-by-side (in parallel)