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

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Particle learning approach to Bayesian model selection: an application from neurology. / Taylor, Simon; Ridall, Gareth; Sherlock, Christopher et al.
The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013. ed. / Ettore Lanzarone; Francesca Leva. Springer, 2014. p. 165-167 (Springer Proceedings in Mathematics and Statistics; Vol. 63).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Taylor, S, Ridall, G, Sherlock, C & Fearnhead, P 2014, Particle learning approach to Bayesian model selection: an application from neurology. in E Lanzarone & F Leva (eds), The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013. Springer Proceedings in Mathematics and Statistics, vol. 63, Springer, pp. 165-167. https://doi.org/10.1007/978-3-319-02084-6_32

APA

Taylor, S., Ridall, G., Sherlock, C., & Fearnhead, P. (2014). Particle learning approach to Bayesian model selection: an application from neurology. In E. Lanzarone, & F. Leva (Eds.), The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013 (pp. 165-167). (Springer Proceedings in Mathematics and Statistics; Vol. 63). Springer. https://doi.org/10.1007/978-3-319-02084-6_32

Vancouver

Taylor S, Ridall G, Sherlock C, Fearnhead P. Particle learning approach to Bayesian model selection: an application from neurology. In Lanzarone E, Leva F, editors, The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013. Springer. 2014. p. 165-167. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-3-319-02084-6_32

Author

Taylor, Simon ; Ridall, Gareth ; Sherlock, Christopher et al. / Particle learning approach to Bayesian model selection : an application from neurology. The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013. editor / Ettore Lanzarone ; Francesca Leva. Springer, 2014. pp. 165-167 (Springer Proceedings in Mathematics and Statistics).

Bibtex

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title = "Particle learning approach to Bayesian model selection: an application from neurology",
abstract = "An improved method is sought to accurately quantify the number of motor units that operate a working muscle. Measurements of a muscle{\textquoteright}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.",
author = "Simon Taylor and Gareth Ridall and Christopher Sherlock and Paul Fearnhead",
year = "2014",
doi = "10.1007/978-3-319-02084-6_32",
language = "English",
isbn = "9783319020839",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer",
pages = "165--167",
editor = "Ettore Lanzarone and Francesca Leva",
booktitle = "The contribution of young researchers to Bayesian statistics",

}

RIS

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T1 - Particle learning approach to Bayesian model selection

T2 - an application from neurology

AU - Taylor, Simon

AU - Ridall, Gareth

AU - Sherlock, Christopher

AU - Fearnhead, Paul

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-02084-6_32

DO - 10.1007/978-3-319-02084-6_32

M3 - Chapter

SN - 9783319020839

T3 - Springer Proceedings in Mathematics and Statistics

SP - 165

EP - 167

BT - The contribution of young researchers to Bayesian statistics

A2 - Lanzarone, Ettore

A2 - Leva, Francesca

PB - Springer

ER -