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Cited by 25Gene Regulatory Network AnalysisMarkov Chains and Monte Carlo MethodsControl Systems and Identification

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Parameter estimation in nonlinear chemical and biological processes with unmeasured variables from small data sets

Jang, S.S., Gopaluni, R.B.·2011·Chemical Engineering Science
DOI 10.1016/j.ces.2011.03.029
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Cites

Research works printed but not linked: 33

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Metadata

Authors
Jang, S. S., & Gopaluni, R. B.
Affiliations
University of British Columbia (CA)
Corresponding author
Gopaluni, R.B.
ORCID
Gopaluni, R.B.
Venue
Chemical Engineering Science
Publisher
Elsevier BV
Published
2011-06
Volume
66
Issue
12
Pages
2774-2787
ISSN
0009-2509 (ISSN-L)
DOI
10.1016/j.ces.2011.03.029
OpenAlex
W2061999684
Cited by
25
References
3
Citations by year
2025: 1 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2018: 3 · 2016: 1 · 2015: 3 · 2014: 3 · 2013: 6
FWCI
1.16
Type
journal article
Language
en
Open access
closed
Licence
Locations
Chemical Engineering Science (publishedVersion)
Funders
National Institutes of Natural Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Keywords
parameter estimation, nonlinear stochastic processes, unmeasured variables, small datasets, Markov chain Monte Carlo, posterior approximation, feed-forward loop, gene regulatory networks, JAK/STAT signaling, chemical process modeling, biological process modeling

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