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pyParticleest : A Python framework for particle-based estimation methods

Nordh, Jerker LU (2017) In Journal of Statistical Software 78.
Abstract

Particle methods such as the particle filter and particle smoothers have proven very useful for solving challenging nonlinear estimation problems in a wide variety of fields during the last decade. However, there are still very few existing tools available to support and assist researchers and engineers in applying the vast number of methods in this field to their own problems. This paper identifies the common operations between the methods and describes a software framework utilizing this information to provide a flexible and extensible foundation which can be used to solve a large variety of problems in this domain, thereby allowing code reuse to reduce the implementation burden and lowering the barrier of entry for applying this... (More)

Particle methods such as the particle filter and particle smoothers have proven very useful for solving challenging nonlinear estimation problems in a wide variety of fields during the last decade. However, there are still very few existing tools available to support and assist researchers and engineers in applying the vast number of methods in this field to their own problems. This paper identifies the common operations between the methods and describes a software framework utilizing this information to provide a flexible and extensible foundation which can be used to solve a large variety of problems in this domain, thereby allowing code reuse to reduce the implementation burden and lowering the barrier of entry for applying this exciting field of methods. The software implementation presented in this paper is freely available and permissively licensed under the GNU Lesser General Public License, and runs on a large number of hardware and software platforms, making it usable for a large variety of scenarios.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Expectation-maximization, Particle filter, Particle smoother, Python, Rao-Blackwellized, System identification
in
Journal of Statistical Software
volume
78
pages
25 pages
external identifiers
  • scopus:85020435054
  • wos:000405335300001
ISSN
1548-7660
DOI
10.18637/jss.v078.i03
language
English
LU publication?
yes
id
34c7e088-faa0-4b1c-abb8-30809d0ab772
date added to LUP
2017-06-29 11:24:53
date last changed
2017-09-18 11:37:01
@article{34c7e088-faa0-4b1c-abb8-30809d0ab772,
  abstract     = {<p>Particle methods such as the particle filter and particle smoothers have proven very useful for solving challenging nonlinear estimation problems in a wide variety of fields during the last decade. However, there are still very few existing tools available to support and assist researchers and engineers in applying the vast number of methods in this field to their own problems. This paper identifies the common operations between the methods and describes a software framework utilizing this information to provide a flexible and extensible foundation which can be used to solve a large variety of problems in this domain, thereby allowing code reuse to reduce the implementation burden and lowering the barrier of entry for applying this exciting field of methods. The software implementation presented in this paper is freely available and permissively licensed under the GNU Lesser General Public License, and runs on a large number of hardware and software platforms, making it usable for a large variety of scenarios.</p>},
  author       = {Nordh, Jerker},
  issn         = {1548-7660},
  keyword      = {Expectation-maximization,Particle filter,Particle smoother,Python,Rao-Blackwellized,System identification},
  language     = {eng},
  pages        = {25},
  series       = {Journal of Statistical Software},
  title        = {pyParticleest : A Python framework for particle-based estimation methods},
  url          = {http://dx.doi.org/10.18637/jss.v078.i03},
  volume       = {78},
  year         = {2017},
}