The pivot algorithm: A highly efficient Monte Carlo method for the self-avoiding walk

1988 ◽  
Vol 50 (1-2) ◽  
pp. 109-186 ◽  
Author(s):  
Neal Madras ◽  
Alan D. Sokal
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
The Self ◽  
Pivot Algorithm ◽  
Highly Efficient ◽  
Self Avoiding Walk

New Monte Carlo method for the self-avoiding walk

1985 ◽  
Vol 40 (3-4) ◽  
pp. 483-531 ◽  
Author(s):  
Alberto Berretti ◽  
Alan D. Sokal
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
The Self ◽  
Self Avoiding Walk

Monte Carlo method for highly efficient and accurate statistical lithography simulations

2002 ◽  
Author(s):  
Sergei V. Postnikov ◽  
Kevin Lucas ◽  
Karl Wimmer ◽  
Vladimir Ivin ◽  
Andrey Rogov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Highly Efficient

DENSITY-OF-STATES MONTE CARLO METHOD FOR SIMULATION OF TWO-DIMENSIONAL RANDOM BOND POTTS MODEL

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2745-2751
Author(s):  
YOU YU ◽  
HE-PING YING ◽  
QING-HU CHEN ◽  
ZHENG-QUAN PAN
Keyword(s):  
Phase Transition ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Thermodynamic Properties ◽  
Density Of States ◽  
Potts Model ◽  
Bimodal Distribution ◽  
The Self ◽  
Two Dimensional ◽  
Finite Lattices

Softening of the phase transition and critical phenomena for the 2D random-bond Potts ferromagnet is investigated by using the density-of-states Monte Carlo method to calculate the thermodynamic properties with a variety of the quenched bond-randomness characterized by a disorder amplitude r=Ks/Kw. The numerical results show that the crossover from the 1st- to 2nd-order transition was induced at finite lattices for the self-dual bimodal distribution.

Empirical analyses of robustness of the square Msplit estimation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Wiśniewski ◽  
Marek Hubert Zienkiewicz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Robust Estimation ◽  
Influence Functions ◽  
Success Rates ◽  
Linear Network ◽  
Outlier Identification ◽  
Highly Efficient ◽  
The Monte Carlo Method ◽  
M Estimation

AbstractThe paper presents Msplit estimation as an alternative to methods in the class of robust M-estimation. The analysis conducted showed that Msplit estimation is highly efficient in the identification of observations encumbered by gross errors, especially those of small or moderate values. The classical methods of robust estimation provide then unsatisfactory results. Msplit estimation also shows high robustness to single gross errors of large values. The presented analysis of Msplit estimators’ robustness is of a chiefly empirical nature and is based on the example of a simulated levelling network and a real angular-linear network. Using the Monte Carlo method, mean success rates for outlier identification were determined and the courses of empirical influence functions were specified. The outcomes of the analysis were compared with the relevant values achieved via selected methods of robust M-estimation.

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