Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Jerker Nordh
Title: pyParticleEst: A Python Framework for Particle-Based Estimation Methods
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 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.

Page views:: 2292. Submitted: 2014-10-15. Published: 2017-06-01.
Paper: pyParticleEst: A Python Framework for Particle-Based Estimation Methods     Download PDF (Downloads: 6184)
pyParticleEst-1.1.3.tar.gz: Python source package Download (Downloads: 187; 139KB) Replication materials Download (Downloads: 144; 12KB)

DOI: 10.18637/jss.v078.i03

This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.