Full Bayesian inference in hidden Markov models of plant growth
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Publication:2080750
DOI10.1214/21-AOAS1594zbMath1498.62260OpenAlexW4297333600MaRDI QIDQ2080750
Paul-Henry Cournède, Gautier Viaud, Yu-Ting Chen
Publication date: 10 October 2022
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/21-aoas1594
Computational methods in Markov chains (60J22) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Plant biology (92C80)
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Cites Work
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- The pseudo-marginal approach for efficient Monte Carlo computations
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- Augmentation schemes for particle MCMC
- An optimal control methodology for plant growth-case study of a water supply problem of sunflower
- Particle MCMC algorithms and architectures for accelerating inference in state-space models
- Sequential Monte Carlo Methods in Practice
- A Strategy for Bayesian Inference for Computationally Expensive Models with Application to the Estimation of Stem Cell Properties
- Some Parameter Estimation Issues in Functional-Structural Plant Modelling
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- Particle Markov Chain Monte Carlo Methods
- Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
- Pseudo-marginal Metropolis–Hastings sampling using averages of unbiased estimators
- Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform
- An adaptive Metropolis algorithm
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