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.
journal_name
Neural Computjournal_title
Neural computationauthors
Yuan K,Niranjan Mdoi
10.1162/neco.2010.07-09-1047subject
Has Abstractpub_date
2010-08-01 00:00:00pages
1993-2001issue
8eissn
0899-7667issn
1530-888Xjournal_volume
22pub_type
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