Parameter distribution estimation in first order ODE

Tianyi Yang; Nguyen Nguyen; Yu Fang Jin; Merry L. Lindsey

doi:10.1109/GENSIPS.2013.6735932

Parameter distribution estimation in first order ODE

Tianyi Yang, Nguyen Nguyen, Yu Fang Jin, Merry L. Lindsey

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

Abstract

With development of new technologies applied to biological experiments, more and more data are generated every day. To make predictions in biological systems, mathematical modeling plays a critical role. Ordinary differential equations (ODEs) contribute to a large portion in mathematical modeling. In which parameters are inevitable. Noise is intrinsic in all experiments. Therefore, to think of parameters as statistical distributions is a realistic treatment. In this paper, we discuss in a 1st order ODE common in biological systems, how to calculate parameter distribution analytically according to the experimentally observed output assumed to be normal distribution. Conditions on when parameter can be correctly estimated are elucidated.

Original language	English (US)
Title of host publication	2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings
Pages	62-65
Number of pages	4
DOIs	https://doi.org/10.1109/GENSIPS.2013.6735932
State	Published - 2013
Externally published	Yes
Event	2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Houston, TX, United States Duration: Nov 17 2013 → Nov 19 2013

Publication series

Name	Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)	2150-3001
ISSN (Electronic)	2150-301X

Other

Other	2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013
Country/Territory	United States
City	Houston, TX
Period	11/17/13 → 11/19/13

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology (miscellaneous)
Computational Theory and Mathematics
Signal Processing
Biomedical Engineering

Access to Document

10.1109/GENSIPS.2013.6735932

Cite this

Yang, T., Nguyen, N., Jin, Y. F., & Lindsey, M. L. (2013). Parameter distribution estimation in first order ODE. In 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings (pp. 62-65). Article 6735932 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). https://doi.org/10.1109/GENSIPS.2013.6735932

Parameter distribution estimation in first order ODE. / Yang, Tianyi; Nguyen, Nguyen; Jin, Yu Fang et al.
2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings. 2013. p. 62-65 6735932 (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics).

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

Yang, T, Nguyen, N, Jin, YF & Lindsey, ML 2013, Parameter distribution estimation in first order ODE. in 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings., 6735932, Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics, pp. 62-65, 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013, Houston, TX, United States, 11/17/13. https://doi.org/10.1109/GENSIPS.2013.6735932

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title = "Parameter distribution estimation in first order ODE",

abstract = "With development of new technologies applied to biological experiments, more and more data are generated every day. To make predictions in biological systems, mathematical modeling plays a critical role. Ordinary differential equations (ODEs) contribute to a large portion in mathematical modeling. In which parameters are inevitable. Noise is intrinsic in all experiments. Therefore, to think of parameters as statistical distributions is a realistic treatment. In this paper, we discuss in a 1st order ODE common in biological systems, how to calculate parameter distribution analytically according to the experimentally observed output assumed to be normal distribution. Conditions on when parameter can be correctly estimated are elucidated.",

author = "Tianyi Yang and Nguyen Nguyen and Jin, {Yu Fang} and Lindsey, {Merry L.}",

year = "2013",

doi = "10.1109/GENSIPS.2013.6735932",

language = "English (US)",

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AB - With development of new technologies applied to biological experiments, more and more data are generated every day. To make predictions in biological systems, mathematical modeling plays a critical role. Ordinary differential equations (ODEs) contribute to a large portion in mathematical modeling. In which parameters are inevitable. Noise is intrinsic in all experiments. Therefore, to think of parameters as statistical distributions is a realistic treatment. In this paper, we discuss in a 1st order ODE common in biological systems, how to calculate parameter distribution analytically according to the experimentally observed output assumed to be normal distribution. Conditions on when parameter can be correctly estimated are elucidated.

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DO - 10.1109/GENSIPS.2013.6735932

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T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

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BT - 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings

T2 - 2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013

Y2 - 17 November 2013 through 19 November 2013

ER -

Parameter distribution estimation in first order ODE

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ASJC Scopus subject areas

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