Search results for "Monte Carlo method."

showing 10 items of 1217 documents

Sample Size Requirements of a Mixture Analysis Method with Applications in Systematic Biology

1999

The available information on sample size requirements of mixture analysis methods is insufficient to permit a precise evaluation of the potential problems facing practical applications of mixture analysis. We use results from Monte Carlo simulation to assess the sample size requirements of a simple mixture analysis method under conditions relevant to biological applications of mixture analysis. The mixture model used includes two univariate normal components with equal variances but assumes that the researcher is ignorant as to the equality of the variances. The method used relies on the EM algorithm to compute the maximum likelihood estimates of the mixture parameters, and the likelihood r…

Statistics and ProbabilityMathematical optimizationGeneral Immunology and MicrobiologyApplied MathematicsMonte Carlo methodUnivariateGeneral MedicineMixture modelGeneral Biochemistry Genetics and Molecular BiologySample size determinationSimple (abstract algebra)Modeling and SimulationLikelihood-ratio testExpectation–maximization algorithmGeneral Agricultural and Biological SciencesAnalysis methodMathematicsJournal of Theoretical Biology
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Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes

1999

A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.

Statistics and ProbabilityMathematical optimizationposterior mode estimatorMarkov chain Monte Carlo methodsMonte Carlo methodBayesian probabilityRejection samplingEstimatorMarkov chain Monte CarloBayesian smoothingGibbs processesHybrid Monte Carlosymbols.namesakeMarquardt algorithmsymbolspair potential functionPair potentialAlgorithmMathematicsGibbs samplingBernoulli
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Multicanonical Monte Carlo simulations

1998

Canonical Monte Carlo simulations of disordered systems like spin glasses and systems undergoing first-order phase transitions are severely hampered by rare event states which lead to exponentially diverging autocorrelation times with increasing system size and hence to exponentially large statistical errors. One possibility to overcome this problem is the multicanonical reweighting method. Using standard local update algorithms it could be demonstrated that the dependence of autocorrelation times on the system size V is well described by a less divergent power law, τ∝Vα, with 1<α<3, depending on the system. After a brief review of the basic ideas, combinations of multicanonical reweighting…

Statistics and ProbabilityMultigrid methodMonte Carlo methodAutocorrelationExponentWang and Landau algorithmStatistical physicsCondensed Matter PhysicsRandom walkPower lawOrder of magnitudeMathematicsPhysica A: Statistical Mechanics and its Applications
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The influence of noise on electron dynamics in semiconductors driven by a periodic electric field

2009

Studies about the constructive aspects of noise and fluctuations in different non-linear systems have shown that the addition of external noise to systems with an intrinsic noise may result in a less noisy response. Recently, the possibility to reduce the diffusion noise in semiconductor bulk materials by adding a random fluctuating contribution to the driving static electric field has been tested. The present work extends the previous theories by considering the noise-induced effects on the electron transport dynamics in low-doped n-type GaAs samples driven by a high-frequency periodic electric field (cyclostationary conditions). By means of Monte Carlo simulations, we calculate the change…

Statistics and ProbabilityNoise powerMaterials scienceField (physics)Cyclostationary processElectric fieldMonte Carlo methodSpectral densityStatistical and Nonlinear PhysicsElectronStatistics Probability and UncertaintyNoise (electronics)Computational physicsJournal of Statistical Mechanics: Theory and Experiment
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Locally Frozen Defects in Random Sequential Adsorption with Diffusional Relaxation

1993

Random sequential adsorption with diffusional relaxation, of two by two square objects on the two-dimensional square lattice is studied by Monte Carlo computer simulation. Asymptotically for large lattice sizes, diffusional relaxation allows the deposition process to reach full coverage. The coverage approaches the full occupation value, 1, as a power-law with convergence exponent near 1/2. For a periodic lattice of finite (even) size $L$, the final state is a frozen random rectangular grid of domain walls connecting single-site defects. The domain sizes saturate at L**0.8. Prior to saturation, i.e., asymptotically for infinite lattice, the domain growth is power-law with growth exponent ne…

Statistics and ProbabilityPeriodic latticeMaterials scienceCondensed matter physicsCondensed Matter (cond-mat)Monte Carlo methodFOS: Physical sciencesCondensed MatterCondensed Matter PhysicsFull coverageSquare latticeRandom sequential adsorptionLattice (order)ExponentDeposition process
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Interfaces in the confined Ising system with competing surface fields

2005

Abstract When a magnetic Ising film is confined in a L × M geometry ( L ⪡ M ) short-range competing magnetic fields ( h 1 ) are applied at opposite walls along the M -direction, a (weakly rounded) localization–delocalization transition of the interface between domains of different orientation that runs parallel to walls can be observed. This transition is the precursor of a wetting phase transition that occurs in the limit of infinite film thickness ( L → ∞ ) at the critical curve T w ( h 1 ) . For T T w ( h 1 ) ( T > T w ( h 1 ) ) such an interface is bound to (unbound from) the walls, while right at T w ( h 1 ) the interface is freely fluctuating around the center of the film. We present …

Statistics and ProbabilityPhysicsCapillary waveMagnetizationDelocalized electronPhase transitionCondensed matter physicsPosition (vector)Monte Carlo methodIsing modelCondensed Matter PhysicsMagnetic fieldPhysica A: Statistical Mechanics and its Applications
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Monte Carlo study of asymmetric 2D XY model

1997

Employing the Polyakov-Susskind approximation in a field theoretical treatment, the t-J model for strongly correlated electrons in two dimensions has recently been shown to map effectively onto an asymmetric two-dimensional classical XY model. The critical temperature at which charge-spin separation occurs in the t-J model is determined by the location of the phase transitions of this effective model. Here we report results of Monte Carlo simulations which map out the complete phase diagram in the two-dimensional parameter space and also shed some light on the critical behaviour of the transitions.

Statistics and ProbabilityPhysicsHybrid Monte CarloQuantum Monte CarloMonte Carlo methodDynamic Monte Carlo methodDiffusion Monte CarloStatistical physicsCondensed Matter PhysicsClassical XY modelCritical exponentMonte Carlo molecular modelingPhysica A: Statistical Mechanics and its Applications
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Phase transition shifts in films

1991

Abstract We present a Monte Carlo computer simulation study of phase transitions in a three-dimensional Ising/lattice gas model with nearest neighbor attractive coupling and confined to a slit-like capillary with absorbing walls. Data are generated for thicknesses D ⩽ 40 and are used to study the shift of the phase boundaries due to finite wall separation.

Statistics and ProbabilityPhysicsPhase transitionCondensed matter physicsCapillary actionLattice (order)Monte Carlo methodIsing modelCondensed Matter Physicsk-nearest neighbors algorithmPhysica A: Statistical Mechanics and its Applications
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Monte Carlo investigations of phase transitions: status and perspectives

2000

Using the concept of finite-size scaling, Monte Carlo calculations of various models have become a very useful tool for the study of critical phenomena, with the system linear dimension as a variable. As an example, several recent studies of Ising models are discussed, as well as the extension to models of polymer mixtures and solutions. It is shown that using appropriate cluster algorithms, even the scaling functions describing the crossover from the Ising universality class to the mean-field behavior with increasing interaction range can be described. Additionally, the issue of finite-size scaling in Ising models above the marginal dimension (d*=4) is discussed.

Statistics and ProbabilityPhysicsPhase transitionStatistical Mechanics (cond-mat.stat-mech)Critical phenomenaMonte Carlo methodCrossoverFOS: Physical sciencesRenormalization groupCondensed Matter PhysicsDimension (vector space)Ising modelStatistical physicsScalingCondensed Matter - Statistical MechanicsPhysica A: Statistical Mechanics and its Applications
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Coupling theory for counterion distributions based in Tsallis statistics

2003

It is well known that the Poisson-Boltzmann (PB) equation yields the exact counterion density around charged objects in the weak coupling limit. In this paper we generalize the PB approach to account for coupling of arbitrary strength by making use of Tsallis q-exponential distributions. Both the weak coupling and the strong coupling limits are reproduced. For arbitrary coupling we also provide simple analytical expressions which are compared to recent Monte Carlo simulations by A. G. Moreira and R. R. Netz [Europhys. Lett. 52 (2000) 705]. Excellent agreement with these is obtained.

Statistics and ProbabilityPhysicschemistry.chemical_classificationCouplingMonte Carlo methodTsallis statisticsFOS: Physical sciencesCondensed Matter - Soft Condensed MatterCondensed Matter PhysicsMean field theorychemistrySimple (abstract algebra)Strong couplingSoft Condensed Matter (cond-mat.soft)Statistical physicsLimit (mathematics)CounterionPhysica A: Statistical Mechanics and its Applications
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