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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).

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Abstract

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:

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ID Code: 49364
Item Type: Journal Article
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 > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 The Authors
Deposited On: 28 Mar 2012 09:08
Last Modified: 10 Apr 2014 09:18

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