PySSM : a Python module for Bayesian inference of linear Gaussian state space models

Strickland, Christopher Mark, Burdett, Robert L., Denham, Robert, & Mengersen, Kerrie L. (2014) PySSM : a Python module for Bayesian inference of linear Gaussian state space models. Journal of Statistical Software, 57(6).

View at publisher (open access)


PySSM is a Python package that has been developed for the analysis of time series using linear Gaussian state space models (SSM). 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 optimised and parallelised Fortran routines. These Fortran routines heavily utilise Basic Linear Algebra (BLAS) and Linear Algebra Package (LAPACK) functions for maximum performance. PySSM contains classes for filtering, classical smoothing as well as simulation smoothing.

Impact and interest:

1 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

462 since deposited on 27 Mar 2012
16 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 49364
Item Type: Journal Article
Refereed: Yes
Keywords: State space models, Software, Python, Kalman filter, Simulation smoother
ISSN: 1548-7660
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Authors
Deposited On: 27 Mar 2012 23:08
Last Modified: 04 Jul 2017 04:16

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page