Thermodynamic limit of the excess internal energy of the fluid phase of a one-component plasma: A Monte Carlo study

1999 ◽  
Vol 111 (14) ◽  
pp. 6538-6547 ◽  
Author(s):  
J. M. Caillol
Keyword(s):  
Monte Carlo ◽  
Internal Energy ◽  
Thermodynamic Limit ◽  
Fluid Phase ◽  
Monte Carlo Study ◽  
Excess Internal Energy ◽  
Component Plasma

MONTE CARLO STUDY OF ONE HOLE IN A QUANTUM ANTIFERROMAGNET

1992 ◽  
Vol 06 (05n06) ◽  
pp. 587-588
Author(s):  
S. Sorella
Keyword(s):  
Magnetic Field ◽  
Monte Carlo ◽  
Thermodynamic Limit ◽  
Quantum Monte Carlo ◽  
Finite Size ◽  
Monte Carlo Study ◽  
Mott Insulator ◽  
Trial Wavefunction ◽  
The One ◽  
Quantum Antiferromagnet

Using the standard Quantum Monte Carlo technique for the Hubbard model, I present here a numerical investigation of the hole propagation in a Quantum Antiferromagnet. The calculation is very well stabilized, using selected sized systems and special use of the trial wavefunction that satisfy the “close shell condition” in presence of an arbitrarily weak Zeeman magnetic field, vanishing in the thermodynamic limit. It will be shown in a forthcoming publication1 that the presence of this magnetic field does not affect thermodynamic properties for the half filled system. Then I have used the same selected sizes for the one hole ground state. I have investigated the question of vanishing or nonvanishing quasiparticle weight, in order to clarify whether the Mott insulator should behave just as conventional insulator with an upper and lower Hubbard band. By comparing the present finite size scaling with several techniques predicting a finite quasiparticle weight (see Fig.1) the data seem more consistent with a vanishing quasiparticle weight, i.e. , as recently suggested by P.W. Anderson2 the Hubbard-Mott insulator should be characterized by non-trivial excitations which cannot be interpreted in a simple quasi-particle picture. However it cannot be excluded , based only on numerical grounds, that a very small but non vanishing quasiparticle weight should survive in the thermodynamic limit.

Monte Carlo Study of a One‐Component Plasma. I

1966 ◽  
Vol 45 (6) ◽  
pp. 2102-2118 ◽  
Author(s):  
S. G. Brush ◽  
H. L. Sahlin ◽  
E. Teller
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Component Plasma

A Monte Carlo study of the classical two-dimensional one-component plasma

1982 ◽  
Vol 28 (2) ◽  
pp. 325-349 ◽  
Author(s):  
J. M. Caillol ◽  
D. Levesque ◽  
J. J. Weis ◽  
J. P. Hansen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Two Dimensional ◽  
Component Plasma

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

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