scholarly journals Overcomplete representation in a hierarchical Bayesian framework

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Monica Pragliola ◽  
Daniela Calvetti ◽  
Erkki Somersalo
Keyword(s):  
Bayesian Framework ◽  
Hierarchical Bayesian ◽  
Overcomplete Representation

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 ◽  
Insurance Premiums

scholarly journals Estimating hunting harvest from partial reporting: a Bayesian approach

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tom Lindström ◽  
Göran Bergqvist
Keyword(s):  
Red Fox ◽  
Predictive Performance ◽  
Bayesian Framework ◽  
Hierarchical Bayesian ◽  
Harvest Rate ◽  
Eurasian Beaver ◽  
Point Estimates ◽  
Random Variability ◽  
Leave One Out ◽  
Analytical Tools

AbstractQuantifying hunting harvest is essential for numerous ecological topics, necessitating reliable estimates. We here propose novel analytical tools for this purpose. Using a hierarchical Bayesian framework, we introduce models for hunting reports that accounts for different structures of the data. Focusing on Swedish harvest reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), European pine marten (Martes martes), and Eurasian beaver (Castor fiber), we evaluated predictive performance through training and validation sets as well as Leave One Out Cross Validation. The analyses revealed that to provide reliable harvest estimates, analyses must account for both random variability among hunting teams and the effect of hunting area per team on the harvest rate. Disregarding the former underestimated the uncertainty, especially at finer spatial resolutions (county and hunting management precincts). Disregarding the latter imposed a bias that overestimated total harvest. We also found support for association between average harvest rate and variability, yet the direction of the association varied among species. However, this feature proved less important for predictive purposes. Importantly, the hierarchical Bayesian framework improved previously used point estimates by reducing sensitivity to low reporting and presenting inherent uncertainties.

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