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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Page views:: 5207. Submitted: 2012-03-23. Published: 2014-04-07.
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Paper: | PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models [size=1em]Download PDF (Downloads: 5736)
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Supplements: | pyssm-1.1e.tar.gz: Python source package | [size=1em]Download(Downloads: 277; 95KB) | pyssm-1.1e.win32-py2.7.exe: Windows 32-bit binary package | [size=1em]Download(Downloads: 254; 2MB) | v57i06-examples.zip: Python example code from the paper | [size=1em]Download(Downloads: 270; 5KB) |
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