A comparison of a generic MCMC-based algorithm for Bayesian estimation in C++, R and Julia: application to plant growth modeling
Abstract
Plant growth is understood through the use of dynamical systems involving many interacting processes and model parameters whose estimation is therefore a crucial issue, all the more so since experimental data obtained from agronomical systems are most of the time characterized by their scarcity and their heterogeneity owing to the complex underlying acquisition. One of the approaches used to solve this kind of problem within a Bayesian paradigm involves Markov Chain Monte Carlo (MCMC) algorithms, one of the drawbacks of the latter being that they lead to some intensive computation because of the statistical framework employed, which is why the efficiency of the implemented computing methods is of particular importance. In this paper, we compare three implementations of a generic MCMC-based algorithm for Bayesian estimation in C++, R and Julia so as to compare the performance and precision of these languages. Here, genericness means that the estimation algorithm can be used for any dynamic model provided that it is implemented in a given modeling template. Such genericness is of crucial importance in a scientific field such as plant growth modeling for which no reference model exists and new models are constantly developed and evaluated. The tests are conducted for the particular cases of Lotka--Volterra model and the Log-Normal Allocation and Senescence model for sugar beet.
References
[1]
C++ website. http://www.cplusplus.com/.
[2]
Julia website. http://julialang.org/.
[3]
R website. http://www.r-project.org/.
[4]
Andrieu, C., Doucet, A., and Holenstein, R. Particle Markov chain Monte Carlo methods. Journal of the Royal Statistical Society 72 (2010), 269--342.
[5]
Berg, B. A., and Billoire, A. Markov chain monte carlo simulations. In Wiley Encyclopedia of Computer Science and Engineering. John Wiley and Sons, Inc., 2007.
[6]
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. Julia: A fresh approach to numerical computing. arXiv:1411.1607v1.
[7]
Bezanson, J., Karpinski, S., Shah, V., and Edelman, A. Julia: A fast dynamic language for technical computing. arXiv:1209.5145v1.
[8]
Brisson, N., Launay, M., Mary, B., and Beaudoin, N. Conceptual basis, formalisations and parameterization of the STICS crop model. Quae �ditions, 2009.
[9]
Campillo, F., and Rossi, V. Convolution Particle Filter for Parameter Estimation in General State-Space Models. IEEE Transactions in Aerospace and Electronics. 45, 3 (2009), 1063--1072.
[10]
Chen, Y. Bayesian Inference in Plant Growth Models for Prediction and Uncertainty Assessment. PhD thesis, Ecole Centrale Paris, 2014.
[11]
Chen, Y., and Courn�de, P.-H. Data assimilation to reduce uncertainty of crop model prediction with convolution particle filtering. Ecological Modelling 290 (2014), 165--177.
[12]
Cournede, P.-H., Chen, Y., Wu, Q., Baey, C., and Bayol, B. Development and evaluation of plant growth models: methodology and implementation in the pygmalion platform. Mathematical Modelling of Natural Phenomena 8, 04 (2013), 112--130.
[13]
de Reffye, P., Heuvelink, E., Barth�l�my, D., and Courn�de, P.-H. Plant growth models. In Ecological Models. Vol. 4 of Encyclopedia of Ecology (5 volumes), S. Jorgensen and B. Fath, Eds. Elsevier, Oxford, 2008, 2824--2837.
[14]
Doucet, A., De Freitas, N., and Gordon, N. Sequential Monte Carlo methods in practice. Springer, New York, 2001.
[15]
Fearnhead, P. MCMC for state-space models. In Handbook of Markov chain Monte Carlo, S. Brooks et al., Eds. Chapman and Hall, London, 2011, 513--529.
[16]
Gilks, W. R. Markov chain monte carlo. In Encyclopedia of Biostatistics. John Wiley and Sons, Ltd, 2005.
[17]
Haario, H., and Tamminen, J. An adaptive Metropolis algorithm. Bernoulli 7 (2001), 223--242.
Index Terms
- A comparison of a generic MCMC-based algorithm for Bayesian estimation in C++, R and Julia: application to plant growth modeling
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Published: 12 April 2015
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