Statistical inference in mechanistic models: time warping for improved gradient matching View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-08-09

AUTHORS

Mu Niu, Benn Macdonald, Simon Rogers, Maurizio Filippone, Dirk Husmeier

ABSTRACT

Inference in mechanistic models of non-linear differential equations is a challenging problem in current computational statistics. Due to the high computational costs of numerically solving the differential equations in every step of an iterative parameter adaptation scheme, approximate methods based on gradient matching have become popular. However, these methods critically depend on the smoothing scheme for function interpolation. The present article adapts an idea from manifold learning and demonstrates that a time warping approach aiming to homogenize intrinsic length scales can lead to a significant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from two dynamical systems with periodic limit cycle, a biopathway, and an application from soft-tissue mechanics. Our study also provides a comparative evaluation on a wide range of signal-to-noise ratios. More... »

PAGES

1091-1123

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00180-017-0753-z

DOI

http://dx.doi.org/10.1007/s00180-017-0753-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091106421

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31258254


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