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), pp. 1993-2001.

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Description/Abstract

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.

Item Type: Article
Organisations: Southampton Wireless Group
ePrint ID: 271146
Date :
Date Event
August 2010Published
Date Deposited: 21 May 2010 13:29
Last Modified: 17 Apr 2017 18:21
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/271146

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