Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Christopher Strickland, Robert Burdett, Kerrie Mengersen, Robert Denham
Title: PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models
Abstract: PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models. PySSM is easy to use; models can be set up quickly and efficiently and a variety of different settings are available to the user. It also takes advantage of scientific libraries NumPy and SciPy and other high level features of the Python language. PySSM is also used as a platform for interfacing between optimized and parallelized Fortran routines. These Fortran routines heavily utilize basic linear algebra and linear algebra Package functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

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Paper: PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models     Download PDF (Downloads: 7991)
Supplements:
pyssm-1.1e.tar.gz: Python source package Download (Downloads: 355; 95KB)
pyssm-1.1e.win32-py2.7.exe: Windows 32-bit binary package Download (Downloads: 306; 2MB)
v57i06-examples.zip: Python example code from the paper Download (Downloads: 317; 5KB)

DOI: 10.18637/jss.v057.i06

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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.