PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models

Autor: Christopher Strickland, Robert Burdett, Kerrie Mengersen, Robert Denham
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Zdroj: Journal of Statistical Software, Vol 57, Iss 1, Pp 1-37 (2014)
Druh dokumentu: article
ISSN: 1548-7660
DOI: 10.18637/jss.v057.i06
Popis: 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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