Parameter distribution estimation in first order ODE

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Proceedings
Pages62-65
Number of pages4
DOIs
StatePublished - 2013
Event2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013 - Houston, TX, United States
Duration: Nov 17 2013Nov 19 2013

Publication series

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

Other

Other2013 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2013
CountryUnited States
CityHouston, TX
Period11/17/1311/19/13

ASJC Scopus subject areas

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

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  • 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). [6735932] (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). https://doi.org/10.1109/GENSIPS.2013.6735932