Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models

Jwala Dhamala; John L. Sapp; Milan Horacek; Linwei Wang

doi:10.1007/978-3-319-59050-9_18

Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models

Jwala Dhamala, John L. Sapp, Milan Horacek, Linwei Wang

Innovative Research and Development Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Original language	English
Title of host publication	Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
Editors	Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz
Publisher	Springer Verlag
Pages	223-235
Number of pages	13
ISBN (Print)	9783319590493
DOIs	https://doi.org/10.1007/978-3-319-59050-9_18
Publication status	Published - 2017
Event	25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States Duration: Jun 25 2017 → Jun 30 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10265 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Information Processing in Medical Imaging, IPMI 2017
Country/Territory	United States
City	Boone
Period	6/25/17 → 6/30/17

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Gaussian process
Markov chain monte carlo
Personalized modeling
Probabilistic parameter estimation

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10.1007/978-3-319-59050-9_18

Cite this

Dhamala, J., Sapp, J. L., Horacek, M., & Wang, L. (2017). Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models. In H. Zhu, M. Niethammer, M. Styner, H. Zhu, D. Shen, P.-T. Yap, S. Aylward, & I. Oguz (Eds.), Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings (pp. 223-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59050-9_18

Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models. / Dhamala, Jwala; Sapp, John L.; Horacek, Milan et al.
Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. ed. / Hongtu Zhu; Marc Niethammer; Martin Styner; Hongtu Zhu; Dinggang Shen; Pew-Thian Yap; Stephen Aylward; Ipek Oguz. Springer Verlag, 2017. p. 223-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Dhamala, J, Sapp, JL, Horacek, M & Wang, L 2017, Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models. in H Zhu, M Niethammer, M Styner, H Zhu, D Shen, P-T Yap, S Aylward & I Oguz (eds), Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10265 LNCS, Springer Verlag, pp. 223-235, 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, United States, 6/25/17. https://doi.org/10.1007/978-3-319-59050-9_18

Dhamala J, Sapp JL, Horacek M, Wang L. Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models. In Zhu H, Niethammer M, Styner M, Zhu H, Shen D, Yap PT, Aylward S, Oguz I, editors, Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Springer Verlag. 2017. p. 223-235. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59050-9_18

Dhamala, Jwala ; Sapp, John L. ; Horacek, Milan et al. / Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo : An application to cardiac electrophysiological models. Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. editor / Hongtu Zhu ; Marc Niethammer ; Martin Styner ; Hongtu Zhu ; Dinggang Shen ; Pew-Thian Yap ; Stephen Aylward ; Ipek Oguz. Springer Verlag, 2017. pp. 223-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Quantifying the uncertainty in model parameters using gaussian process-based markov chain monte carlo: An application to cardiac electrophysiological models

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Keywords

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