Yuan, K. and Niranjan, M. (2010) Estimating a state-space model from point process observations: a note on convergence. Neural Computation, 22(8), pp. 1993-2001. (doi: 10.1162/neco.2010.07-09-1047) (PMID:20337540)
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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: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Yuan, Dr Ke |
| Authors: | Yuan, K., and Niranjan, M. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | Neural Computation |
| Publisher: | MIT Press |
| ISSN: | 0899-7667 |
| ISSN (Online): | 1530-888X |
| Published Online: | 24 June 2010 |
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