# Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE

E. Grenier; V. Louvet; P. Vigneaux

- Volume: 48, Issue: 5, page 1303-1329
- ISSN: 0764-583X

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topGrenier, E., Louvet, V., and Vigneaux, P.. "Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE." ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique 48.5 (2014): 1303-1329. <http://eudml.org/doc/273171>.

@article{Grenier2014,

abstract = {Parameter estimation in non linear mixed effects models requires a large number of evaluations of the model to study. For ordinary differential equations, the overall computation time remains reasonable. However when the model itself is complex (for instance when it is a set of partial differential equations) it may be time consuming to evaluate it for a single set of parameters. The procedures of population parametrization (for instance using SAEM algorithms) are then very long and in some cases impossible to do within a reasonable time. We propose here a very simple methodology which may accelerate population parametrization of complex models, including partial differential equations models. We illustrate our method on the classical KPP equation.},

author = {Grenier, E., Louvet, V., Vigneaux, P.},

journal = {ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique},

keywords = {parameter estimation; SAEM algorithm; partial differential equations; KPP equation; SAEM (stochastic approximation expectation-maximization) algorithm; (Kolmogorov-Petrovskii-Piscunov) KPP equation},

language = {eng},

number = {5},

pages = {1303-1329},

publisher = {EDP-Sciences},

title = {Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE},

url = {http://eudml.org/doc/273171},

volume = {48},

year = {2014},

}

TY - JOUR

AU - Grenier, E.

AU - Louvet, V.

AU - Vigneaux, P.

TI - Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE

JO - ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

PY - 2014

PB - EDP-Sciences

VL - 48

IS - 5

SP - 1303

EP - 1329

AB - Parameter estimation in non linear mixed effects models requires a large number of evaluations of the model to study. For ordinary differential equations, the overall computation time remains reasonable. However when the model itself is complex (for instance when it is a set of partial differential equations) it may be time consuming to evaluate it for a single set of parameters. The procedures of population parametrization (for instance using SAEM algorithms) are then very long and in some cases impossible to do within a reasonable time. We propose here a very simple methodology which may accelerate population parametrization of complex models, including partial differential equations models. We illustrate our method on the classical KPP equation.

LA - eng

KW - parameter estimation; SAEM algorithm; partial differential equations; KPP equation; SAEM (stochastic approximation expectation-maximization) algorithm; (Kolmogorov-Petrovskii-Piscunov) KPP equation

UR - http://eudml.org/doc/273171

ER -

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