Inductive process modeling

DOI10.1007/s10994-007-5042-6zbMath1470.68088OpenAlexW2055368398MaRDI QIDQ1009252

Will Bridewell, Sašo Džeroski, Pat Langley, Ljupčo Todorovski

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-007-5042-6

zbMATH Keywords

system identificationecosystem modelingscientific discoverycompositional modelingprocess models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) System identification (93B30) Ecology (92D40) General theory of mathematical modeling (00A71)

Related Items (2)

Combined Bayesian Networks and Rough-Granular Approaches for Discovery of Process Models Based on Vehicular Traffic Simulation ⋮ Bayesian learning of stochastic dynamical models

Uses Software

• CVODE
• nlr
• Algorithm 611
• bootstrap

Cites Work

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• Causality and model abstraction
• System identification. A survey
• Algorithm 611: Subroutines for Unconstrained Minimization Using a Model/Trust-Region Approach
• An Adaptive Nonlinear Least-Squares Algorithm
• Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models
• Bootstrap Methods for Time Series
• Enhancing the plausibility of law equation discovery through cross check among multiple scale-type-based models
• Spurious Correlation: A Causal Interpretation
• Reasoning about nonlinear system identification

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