Inference and learning in state-space point process models:
algorithms and applications
Inference and learning in state-space point process models:
algorithms and applications
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.
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
May 2013
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Yuan, Ke
(2013)
Inference and learning in state-space point process models:
algorithms and applications.
University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 203pp.
Record type:
Thesis
(Doctoral)
Abstract
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.
More information
Published date: May 2013
Organisations:
University of Southampton, Southampton Wireless Group
Identifiers
Local EPrints ID: 352932
URI: http://eprints.soton.ac.uk/id/eprint/352932
PURE UUID: b4fbf7a0-b5d1-4fb5-b97a-a0ceea2598f9
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Date deposited: 28 May 2013 10:42
Last modified: 15 Mar 2024 03:29
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Contributors
Author:
Ke Yuan
Thesis advisor:
Mahesan Niranjan
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