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Estimating a State-Space Model from Point Process Observations: A Note on Convergence

Record type: Article

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.

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Citation

Yuan, Ke and Niranjan, Mahesan (2010) Estimating a State-Space Model from Point Process Observations: A Note on Convergence Neural Computation, 22, (8), pp. 1993-2001.

More information

Published date: August 2010
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271146
URI: http://eprints.soton.ac.uk/id/eprint/271146
PURE UUID: 801902fc-8961-4f0b-9f79-71965627ce92

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Date deposited: 21 May 2010 13:29
Last modified: 18 Jul 2017 06:46

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Contributors

Author: Ke Yuan

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