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


Autoria(s): Strickland, Christopher Mark; Burdett, Robert L.; Denham, Robert; Mengersen, Kerrie L.
Data(s)

2014

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/49364/

Publicador

American Statistical Association

Relação

http://eprints.qut.edu.au/49364/4/49364.pdf

http://www.jstatsoft.org/v57/i06/paper

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).

http://purl.org/au-research/grants/ARC/LP100100565

Direitos

Copyright 2014 The Authors

Fonte

School of Mathematical Sciences; Science & Engineering Faculty

Palavras-Chave #010400 STATISTICS #State space models #Software #Python #Kalman filter #Simulation smoother
Tipo

Journal Article