A regularized particle filter EM algorithm based on Gaussian randomization with an application to plant growth modeling

DOI10.1007/S11009-015-9440-0zbMath1347.60139OpenAlexW2085824023MaRDI QIDQ905216

Samis Trevezas, Paul-Henry Cournède, Yu-Ting Chen

Publication date: 14 January 2016

Published in: Methodology and Computing in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11009-015-9440-0

zbMATH Keywords

state space modelstochastic EM algorithmplant growth modelGaussian randomizationLNAS modelregularized particle filter

Mathematics Subject Classification ID

Gaussian processes (60G15) Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric inference (62F99) Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Special processes (60K99)

Related Items (1)

Bayesian estimation for the GreenLab plant growth model with deterministic organogenesis

Uses Software

• PYGMALION

Cites Work

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• A sequential Monte Carlo approach for MLE in a plant growth model
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• Convergence of a stochastic approximation version of the EM algorithm
• Inference in hidden Markov models.
• Sequential Monte Carlo Methods in Practice
• Maximum likelihood estimation via the ECM algorithm: A general framework
• The EM Algorithm and Extensions, 2E
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

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