scholarly journals A Bayesian Inference Framework for Procedural Material Parameter Estimation

2020 ◽  
Vol 39 (7) ◽  
pp. 255-266
Author(s):  
Y. Guo ◽  
M. Hašan ◽  
L. Yan ◽  
S. Zhao
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Material Parameter ◽  
Bayesian Inference Framework

Likelihood-free Bayesian inference framework for sizing kiwifruit from orchard imaging surveys

2021 ◽  
Author(s):  
Louis Ranjard ◽  
James Bristow ◽  
Zulfikar Hossain ◽  
Alvaro Orsi ◽  
Henry J. Kirkwood ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Bayesian Inference Framework

scholarly journals An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics

2015 ◽  
Vol 31 (20) ◽  
pp. 3282-3289 ◽  
Author(s):  
Shiwei Lan ◽  
Julia A. Palacios ◽  
Michael Karcher ◽  
Vladimir N. Minin ◽  
Babak Shahbaba
Keyword(s):  
Bayesian Inference ◽  
Bayesian Inference Framework

Porous material parameter estimation: A Bayesian approach

2012 ◽  
Author(s):  
Cameron Fackler ◽  
Eric Dieckman ◽  
Ning Xiang
Keyword(s):  
Parameter Estimation ◽  
Porous Material ◽  
Bayesian Approach ◽  
Material Parameter

scholarly journals Wilson statistics: derivation, generalization and applications to electron cryomicroscopy

2021 ◽  
Vol 77 (5) ◽  
Author(s):  
Amit Singer
Keyword(s):  
Bayesian Inference ◽  
Theoretical Foundation ◽  
Three Dimensional ◽  
Electron Cryomicroscopy ◽  
High Frequencies ◽  
X Ray ◽  
X Ray Crystallography ◽  
Bayesian Inference Framework ◽  
Higher Order Spectra ◽  
Mathematical Derivation

The power spectrum of proteins at high frequencies is remarkably well described by the flat Wilson statistics. Wilson statistics therefore plays a significant role in X-ray crystallography and more recently in electron cryomicroscopy (cryo-EM). Specifically, modern computational methods for three-dimensional map sharpening and atomic modelling of macromolecules by single-particle cryo-EM are based on Wilson statistics. Here the first rigorous mathematical derivation of Wilson statistics is provided. The derivation pinpoints the regime of validity of Wilson statistics in terms of the size of the macromolecule. Moreover, the analysis naturally leads to generalizations of the statistics to covariance and higher-order spectra. These in turn provide a theoretical foundation for assumptions underlying the widespread Bayesian inference framework for three-dimensional refinement and for explaining the limitations of autocorrelation-based methods in cryo-EM.

scholarly journals A comparison of Bayesian inference and gradient-based approaches for friction parameter estimation

2020 ◽  
Author(s):  
Simon Warder ◽  
Athanasios Angeloudis ◽  
Stephan Kramer ◽  
Colin Cotter ◽  
Matthew Piggott
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Friction Parameter ◽  
Gradient Based
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