Name
Title | ||
|---|---|---|
Estimating a state-space model from point process observations: a note on convergence. |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1162/neco.2010.07-09-1047 | Neural Computation |
Keywords | Field | DocType |
optimal solution,em iteration,data-driven model,neural spike,discrete event,point process observation,maximum likelihood,underlying state sequence,physiological signal,continuous underlying system,state-space model,state space model,em algorithm,expectation maximization,point process,global optimization | Convergence (routing),Mathematical optimization,Expectation–maximization algorithm,State-space representation,Point process,Models of neural computation,Algorithm,Discrete time and continuous time,Artificial neural network,State space,Mathematics,Calculus | Journal |
Volume | Issue | ISSN |
22 | 8 | 1530-888X |
Citations | PageRank | References |
4 | 0.56 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Yuan | 1 | 29 | 3.80 |
| Mahesan Niranjan | 2 | 775 | 120.43 |