A Markov Chain Monte Carlo technique for parameter estimation and inference in pesticide fate and transport modeling

Julien Boulange, Hirozumi Watanabe and Shinpei Akai

Ecological Modelling, 2017, vol. 360, issue C, 270-278

Abstract: A Bayesian method involving Markov Chain Monte Carlo (MCMC) technique was implemented into a pesticide fate and transport model to estimate the best input parameter ranges while considering uncertainties included in both the observed pesticide concentrations and in the model.

Keywords: Rice paddy; Pesticide fate and transport; Markov Chain Monte Carlo (MCMC); Inverse modeling; PCPF-1 model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:360:y:2017:i:c:p:270-278

DOI: 10.1016/j.ecolmodel.2017.07.011

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