Use this URL to cite or link to this record in EThOS:
Title: Inference and learning in state-space point process models : algorithms and applications
Author: Yuan, Ke
ISNI:       0000 0004 2734 5228
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Access from Institution:
Physiological signals such as neural spikes and heart beats are discrete events in time, driven by a continuous underlying system. A recently introduced data driven model to analyse such systems is the state-space model with point process observations (SSPP), parameters of which and the underlying state sequence are simultaneously identified in a maximum likelihood setting using an approximate expectation-maximization (EM) algorithm. This thesis provides a detailed study on the property of SSPP under the EM setting. The results strongly suggest that the Bayesian treatment is more appropriate to avoid biased estimation. For this we develop the variational methods, and a range of efficient Markov chain Monte Carlo methods. The performance of these inference mechanisms is thoroughly tested on both synthetic and real world datasets.
Supervisor: Niranjan, Mahesan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA75 Electronic computers. Computer science