Rejoinder to ‘Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework’

2016 ◽  
Vol 32 (3) ◽  
pp. 357-357
Author(s):  
Hongxia Yang ◽  
Robert Ormandi ◽  
Han-Yun Tsao ◽  
Quan Lu
Keyword(s):  
Rare Events ◽  
Bayesian Framework ◽  
Hierarchical Bayesian

Estimating rates of rare events through a multidimensional dynamic hierarchical Bayesian framework

2016 ◽  
Vol 32 (3) ◽  
pp. 340-353 ◽  
Author(s):  
Hongxia Yang ◽  
Robert Ormandi ◽  
Han-Yun Tsao ◽  
Quan Lu
Keyword(s):  
Rare Events ◽  
Bayesian Framework ◽  
Hierarchical Bayesian

scholarly journals A hierarchical Bayesian framework for force field selection in molecular dynamics simulations

2016 ◽  
Vol 374 (2060) ◽  
pp. 20150032 ◽  
Author(s):  
S. Wu ◽  
P. Angelikopoulos ◽  
C. Papadimitriou ◽  
R. Moser ◽  
P. Koumoutsakos
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Force Field ◽  
Computational Cost ◽  
Md Simulations ◽  
Bayesian Framework ◽  
Underlying Structure ◽  
Hierarchical Bayesian ◽  
Wide Range ◽  
Selection Of

We present a hierarchical Bayesian framework for the selection of force fields in molecular dynamics (MD) simulations. The framework associates the variability of the optimal parameters of the MD potentials under different environmental conditions with the corresponding variability in experimental data. The high computational cost associated with the hierarchical Bayesian framework is reduced by orders of magnitude through a parallelized Transitional Markov Chain Monte Carlo method combined with the Laplace Asymptotic Approximation. The suitability of the hierarchical approach is demonstrated by performing MD simulations with prescribed parameters to obtain data for transport coefficients under different conditions, which are then used to infer and evaluate the parameters of the MD model. We demonstrate the selection of MD models based on experimental data and verify that the hierarchical model can accurately quantify the uncertainty across experiments; improve the posterior probability density function estimation of the parameters, thus, improve predictions on future experiments; identify the most plausible force field to describe the underlying structure of a given dataset. The framework and associated software are applicable to a wide range of nanoscale simulations associated with experimental data with a hierarchical structure.

Hierarchical Bayesian Framework for Bus Dwell Time Prediction

2020 ◽  
pp. 1-10
Author(s):  
Isaac K. Isukapati ◽  
Conor Igoe ◽  
Eli Bronstein ◽  
Viraj Parimi ◽  
Stephen F. Smith
Keyword(s):  
Dwell Time ◽  
Bayesian Framework ◽  
Hierarchical Bayesian ◽  
Time Prediction

A note on computing bonus-malus insurance premiums using a hierarchical bayesian framework

Test ◽  
2006 ◽  
Vol 15 (2) ◽  
pp. 345-359 ◽  
Author(s):  
E. Gómez-Déniz ◽  
F. J. Vázquez-Polo ◽  
J. M. Pérez
Keyword(s):  
Bayesian Framework ◽  
Hierarchical Bayesian ◽  
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