scholarly journals Efficiencies of dynamic Monte Carlo algorithms for off-lattice particle systems with a single impurity

2010 ◽  
Vol 3 (3) ◽  
pp. 1481-1485
Author(s):  
M.A. Novotny ◽  
Hiroshi Watanabe ◽  
Nobuyasu Ito
2021 ◽  
Vol 8 ◽  
Author(s):  
Nima Vakili ◽  
Michael Habeck

Random tomography is a common problem in imaging science and refers to the task of reconstructing a three-dimensional volume from two-dimensional projection images acquired in unknown random directions. We present a Bayesian approach to random tomography. At the center of our approach is a meshless representation of the unknown volume as a mixture of spherical Gaussians. Each Gaussian can be interpreted as a particle such that the unknown volume is represented by a particle cloud. The particle representation allows us to speed up the computation of projection images and to represent a large variety of structures accurately and efficiently. We develop Markov chain Monte Carlo algorithms to infer the particle positions as well as the unknown orientations. Posterior sampling is challenging due to the high dimensionality and multimodality of the posterior distribution. We tackle these challenges by using Hamiltonian Monte Carlo and a global rotational sampling strategy. We test the approach on various simulated and real datasets.


1988 ◽  
Vol 102 ◽  
pp. 79-81
Author(s):  
A. Goldberg ◽  
S.D. Bloom

AbstractClosed expressions for the first, second, and (in some cases) the third moment of atomic transition arrays now exist. Recently a method has been developed for getting to very high moments (up to the 12th and beyond) in cases where a “collective” state-vector (i.e. a state-vector containing the entire electric dipole strength) can be created from each eigenstate in the parent configuration. Both of these approaches give exact results. Herein we describe astatistical(or Monte Carlo) approach which requires onlyonerepresentative state-vector |RV> for the entire parent manifold to get estimates of transition moments of high order. The representation is achieved through the random amplitudes associated with each basis vector making up |RV>. This also gives rise to the dispersion characterizing the method, which has been applied to a system (in the M shell) with≈250,000 lines where we have calculated up to the 5th moment. It turns out that the dispersion in the moments decreases with the size of the manifold, making its application to very big systems statistically advantageous. A discussion of the method and these dispersion characteristics will be presented.


2021 ◽  
pp. 108041
Author(s):  
C.U. Schuster ◽  
T. Johnson ◽  
G. Papp ◽  
R. Bilato ◽  
S. Sipilä ◽  
...  

2016 ◽  
Vol 11 (1) ◽  
pp. 1600039 ◽  
Author(s):  
Dimitrios Meimaroglou ◽  
Prokopios Pladis ◽  
Costas Kiparissides

2003 ◽  
Vol 62 (3-6) ◽  
pp. 289-295 ◽  
Author(s):  
V.N. Alexandrov ◽  
I.T. Dimov ◽  
A. Karaivanova ◽  
C.J.K. Tan

Polymer ◽  
2006 ◽  
Vol 47 (8) ◽  
pp. 2928-2932 ◽  
Author(s):  
Jianhua Huang ◽  
Zhaofeng Mao ◽  
Changji Qian

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