scholarly journals Parameter estimation of gravitational waves from nonprecessing black hole-neutron star inspirals with higher harmonics: Comparing Markov-chain Monte Carlo posteriors to an effective Fisher matrix

2014 ◽  
Vol 89 (6) ◽  
Author(s):  
Richard O’Shaughnessy ◽  
Ben Farr ◽  
Evan Ochsner ◽  
Hee-Suk Cho ◽  
Chunglee Kim ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Black Hole ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Neutron Star ◽  
Gravitational Waves ◽  
Higher Harmonics ◽  
Fisher Matrix

scholarly journals Parameter estimation of gravitational waves from precessing black hole-neutron star inspirals with higher harmonics

2014 ◽  
Vol 89 (10) ◽  
Author(s):  
Richard O’Shaughnessy ◽  
Benjamin Farr ◽  
Evan Ochsner ◽  
Hee-Suk Cho ◽  
V. Raymond ◽  
...  
Keyword(s):  
Black Hole ◽  
Parameter Estimation ◽  
Neutron Star ◽  
Gravitational Waves ◽  
Higher Harmonics

scholarly journals Gravitational waves from black hole-neutron star binaries: Effective Fisher matrices and parameter estimation using higher harmonics

2013 ◽  
Vol 87 (2) ◽  
Author(s):  
Hee-Suk Cho ◽  
Evan Ochsner ◽  
Richard O’Shaughnessy ◽  
Chunglee Kim ◽  
Chang-Hwan Lee
Keyword(s):  
Black Hole ◽  
Parameter Estimation ◽  
Neutron Star ◽  
Gravitational Waves ◽  
Higher Harmonics

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.

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