A numerically efficient implementation of the expectation maximization algorithm for state space models

Empirical time series are subject to observational noise. Naïve approaches that estimate parameters in stochastic models for such time series are likely to fail due to the error-in-variables challenge. State space models (SSM) explicitly include observational noise. Applying the expectation maximiza...

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Vydané v:	Applied mathematics and computation Ročník 241; s. 222 - 232
Hlavní autori:	Mader, Wolfgang, Linke, Yannick, Mader, Malenka, Sommerlade, Linda, Timmer, Jens, Schelter, Björn
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 15.08.2014
Predmet:	Algorithms Computation Estimates Expectation–maximization algorithm Kalman filter Mathematical models Maximization Noise Parameter estimation State space models State-space model Time series Parameter estimation Expectation–maximization algorithm State-space model Kalman filter
ISSN:	0096-3003, 1873-5649
On-line prístup:	Získať plný text
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