Estimating a State-Space Model from Point Process Observations: A Note on Convergence
Yuan, Ke and Niranjan, Mahesan (2010) Estimating a State-Space Model from Point Process Observations: A Note on Convergence. Neural Computation, 22, (8), 1993-2001.
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Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneously identified in a maximum likelihood setting using the expectation-maximization (EM) algorithm. In this note, we observe some simple convergence properties of such a setting, previously un-noticed. Simulations show that the likelihood is unimodal in the unknown parameters, and hence the EM iterations are always able to find the globally optimal solution.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||21 May 2010 13:29|
|Last Modified:||24 Jul 2012 03:39|
|Contributors:||Yuan, Ke (Author)
Niranjan, Mahesan (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||3|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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