PySSM : a Python module for Bayesian inference of linear Gaussian state space models
Strickland, Christopher Mark, Burdett, Robert L., Denham, Robert, & Mengersen, Kerrie L. (2012) PySSM : a Python module for Bayesian inference of linear Gaussian state space models. [Working Paper] (Submitted (not yet accepted for publication))
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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.
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| ID Code: | 49364 |
|---|---|
| Item Type: | Working Paper |
| Keywords: | State space models, Software, Python, Kalman filter, Simulation smoother |
| 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 2012 The Authors |
| Deposited On: | 28 Mar 2012 09:08 |
| Last Modified: | 28 Mar 2012 09:27 |
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