Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling

DOI10.1016/j.csda.2014.04.004zbMath1506.62179OpenAlexW2093397455MaRDI QIDQ1623627

Samis Trevezas, Paul-Henry Cournède, Sonia Malefaki

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.04.004

zbMATH Keywords

hidden Markov modelMetropolis-within-Gibbsplant growth modelautomated Monte Carlo EM algorithmMonte Carlo ECM-type algorithmsequential importance sampling with resampling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40) Plant biology (92C80)

Related Items (4)

Bayesian estimation for the GreenLab plant growth model with deterministic organogenesis ⋮ Indicator-based Bayesian variable selection for Gaussian process models in computer experiments ⋮ A regularized particle filter EM algorithm based on Gaussian randomization with an application to plant growth modeling ⋮ Mixed-effects estimation in dynamic models of plant growth for the assessment of inter-individual variability

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