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

2017 ◽  
Vol 360 ◽  
pp. 270-278 ◽  
Author(s):  
Julien Boulange ◽  
Hirozumi Watanabe ◽  
Shinpei Akai
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Fate And Transport ◽  
Monte Carlo Technique ◽  
Transport Modeling ◽  
Fate And Transport Modeling

scholarly journals Fast and reliable Markov chain Monte Carlo technique for cosmological parameter estimation

2005 ◽  
Vol 356 (3) ◽  
pp. 925-936 ◽  
Author(s):  
Joanna Dunkley ◽  
Martin Bucher ◽  
Pedro G. Ferreira ◽  
Kavilan Moodley ◽  
Constantinos Skordis
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Cosmological Parameter ◽  
Monte Carlo Technique

A novel approach to model the land mobile satellite channel through reversible jump markov chain monte carlo technique

2008 ◽  
Vol 7 (2) ◽  
pp. 532-542 ◽  
Author(s):  
Clemence Alasseur ◽  
Sandro Scalise ◽  
Lionel Husson ◽  
Harald Ernst
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Technique ◽  
Reversible Jump ◽  
Novel Approach ◽  
Mobile Satellite

scholarly journals Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 606
Author(s):  
Alaa Jamal ◽  
Raphael Linker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Sequential Estimation ◽  
Measurement Noise ◽  
Genetic Operators ◽  
True Value

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

Parameter estimation: Markov Chain Monte Carlo

2017 ◽  
pp. 173-203
Author(s):  
Coryn A. L. Bailer-Jones
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo

scholarly journals Parameter Estimation of Extreme Rainfall Distribution in Johor using Bayesian Markov Chain Monte Carlo

2020 ◽  
Vol 479 ◽  
pp. 012019
Author(s):  
N Nazmi ◽  
H F S Saipol ◽  
F Yusof ◽  
S A Mazlan ◽  
M A A Rahman ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Extreme Rainfall ◽  
Rainfall Distribution
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