Monte-Carlo based techniques
Chiral 2N and 3N interactions and quantum Monte Carlo applications
... Quantum Monte Carlo methods (namely Green’s function Monte Carlo (GFMC) [15–17] and Auxiliary-Field Diffusion Monte carlo (AFDMC) [18]), did ...that Monte Carlo ...
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Stochastic comparisons of stratied sampling techniques for some Monte Carlo estimators
... The paper is organized as follows. Section 2 fixes notation and reviews various properties of stochastic orders and certain dependence structures. Section 3 compares estimators of the supremum of a function based ...
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Monte Carlo inference and maximization for phrase based translation
... using Monte Carlo techniques we avoid the biases associ- ated with the more commonly used DP based max- derivation (or k -best derivation) ...
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Bayesian Estimation Using MCMC Approach Based on Progressive First-Failure Censoring from Generalized Pareto Distribution
... Chain Monte Carlo (MCMC) techniques to compute the credible intervals and bootstrap confidence intervals of the unknown parameters of Lomax distribution under the progressive first-failure censoring ...
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Advanced Optimization Techniques For Monte Carlo Simulation On Graphics Processing Units
... Although in this level of parallelism kernel calls are independent to some extent, the need for a syn- chronization mechanism at the end to calculate final results is required, referred to as synchronization barrier in ...
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Household water use and conservation models using Monte Carlo techniques
... empirical techniques for house- hold water demand analyses, this paper presents a more de- ductive (“causal”) household end-use model based on phys- ical parameters affecting water use that vary by ...a ...
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A Monte Carlo based analysis of optimal design criteria
... The statistical model we assume is a classical first model widely found in regression analysis [13, 23] and is often employed in development of ideas (asymptotic distribution theory, statistical comparison tests, etc.) ...
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Evaluation of Techniques for Univariate Normality Test Using Monte Carlo Simulation
... normality techniques using power of test by simulating data from desirable ...methods based on sample size and in terms of peakedness and skewness of the ...
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Examples of Monte Carlo techniques applied for nuclear data uncertainty propagation
... is based on first order perturbation ...Different techniques have been developed for the determination of the sensitivity coefficients of the neutronics parameters to nuclear data such as the Standard ...
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Advances in Monte Carlo techniques with application to lattice protein aggregation
... Actually, the mathematical formulation of the theory of MBAR estimator was based on the work of statisticians [33, 47, 59]. When Bennett [3] published the acceptance ratio method for free energy calculation, it ...
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Criticality Analysis and Quality Appraisal of Innoson Injection Mould System
... maintenance techniques that is based on systems and process that support global ...employed Monte Carlo Normal distribution model which interacts with a developed Obudulu model to assess ...
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A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks
... geometrical techniques, multidi- mensional scaling, stochastic proximity embedding, convex and nonconvex optimization, and ...obtained based on three edge-measuring or maximum likelihood methods ...
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Monte Carlo-based quantitative pinhole SPECT reconstruction using a ray-tracing back-projector
... material, and typical reconstruction times for a heart phantom (50 iterations) were roughly 10 and 20 h for empty and water-filled torsos, respectively. The majority of the reconstruction time is spent in the MC ...
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Pricing Callable Bonds Based on Monte Carlo Simulation Techniques
... paper, based on these new simulation techniques we present a Monte Carlo method to numerically price the Bermudan-type callable bond with ...
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Bayesian Learning of Asymmetric Gaussian-Based Statistical Models using Markov Chain Monte Carlo Techniques
... Chain Monte Carlo (MCMC) based implementations, are found to be useful to eliminate overfitting problems in mixture parameter learning by introduc- ing prior and posterior distributions for mixture ...
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Optimal Mortgage Refinancing Based on Monte Carlo Simulation
... analytical techniques for characterizing option contracts, if possible, usually require mathematically strong and sometimes parameter sensitive properties attached to the formulation of the problem, such as the ...
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Stochastic gradient Markov chain Monte Carlo
... for Monte Carlo sampling, which is known as the unadjusted Langevin ...MCMC techniques, such as piece- wise deterministic MCMC (Fearnhead et ...
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Some Ridge Regression Estimators and Their Performances
... studied. Based on the results of simulations and numerical examples, estimators HSL, AM, GM, MED, KS_MAX, KM2, KM3, KM5, KM8, KM9, KMO, CJH, FG, and proposed KB1, KB2, KB3, KB4, and KB5 performed better than the ...
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Monte Carlo Simulation
... We know from Discrete Time Finance that one can compute a fair price for an option by taking an expectation.. E Q e − rT X.[r] ...
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Monte Carlo methods
... distribution. Monte Carlo methods are sampling algorithms that allow to com- pute these integrals numerically when they are not analytically ...common Monte Carlo algorithms, among which ...
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