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Particle learning approach to Bayesian model selection: an application from neurology

Research output: Contribution in Book/Report/ProceedingsChapter

Published

Publication date2014
Host publicationThe contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013
EditorsEttore Lanzarone, Francesca Leva
PublisherSpringer
Pages165-167
Number of pages3
ISBN (Electronic)9783319020846
ISBN (Print)9783319020839
Original languageEnglish

Publication series

NameSpringer Proceedings in Mathematics and Statistics
PublisherSpringer
Volume63
ISSN (Print)2194-1009

Abstract

An improved method is sought to accurately quantify the number of motor units that operate a working muscle. Measurements of a muscle’s contractive potential are obtained by administering a sequence of electrical stimuli. However, the firing patterns of the motor units are non-deterministic and therefore estimating their number is non-trivial. We consider a state-space model that assumes a fixed number of motor units to describe the hidden processes within the body. Particle learning methodology is applied to estimate the marginal likelihood for a range of models that assumes a different number of motor units. Simulation studies of these systems, containing up to 5 motor units, are very promising.