Results 51 to 60 of about 185,877 (166)
Monte Carlo methods for linear and non-linear Poisson-Boltzmann equation*
ESAIM: Proceedings and Surveys, 2015 The electrostatic potential in the neighborhood of a biomolecule can be computed thanks to the non-linear divergence-form elliptic Poisson-Boltzmann PDE.
Bossy Mireille +5 more
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Random Number Generation and Monte Carlo Methods (2nd edition)
Journal of Statistical Software, 2004 s not available for ...
Rodney Sparapani
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Monte Carlo Solutions for Blind Phase Noise Estimation
EURASIP Journal on Wireless Communications and Networking, 2009 This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN) channels. The main contributions of the paper are (i) the development of a Monte Carlo framework for phase noise ...
Çırpan Hakan +4 more
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Multilevel Monte Carlo Methods [PDF]
, 2001 We study Monte Carlo approximations to high dimensional parameter dependent integrals. We survey the multilevel variance reduction technique introduced by the author in [4] and present extensions and new developments of it. The tools needed for the convergence analysis of vector-valued Monte Carlo methods are discussed, as well.
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Method “Monte Carlo” in healthcare
World Journal of MethodologyIn public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions.
Velikova, Tsvetelina +2 more
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Stochastic Assessment of Voltage Sags in Distribution Networks
Iranian Journal of Electrical and Electronic Engineering, 2008 This paper compares fault position and Monte Carlo methods as the most common methods in stochastic assessment of voltage sags. To compare their abilities, symmetrical and unsymmetrical faults with different probability distribution of fault positions ...
M. Aliakbar-Golkar, Y. Raisee-Gahrooyi
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This survey explores the development of adjoint Monte Carlo methods for solving optimization problems governed by kinetic equations, a common challenge in areas such as plasma control and device design. These optimization problems are particularly demanding due to the high dimensionality of the phase space and the randomness in evaluating the objective
Russel Caflisch, Yunan Yang
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How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics
MethodsX, 2018 We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release ...
Paola Lecca
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Lecturas de Economía, 2009 El método Monte Carlo se aplica a varios casos de valoración de opciones financieras. El método genera una buena aproximación al comparar su precisión con la de otros métodos numéricos.
Cecilia Maya
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The moment‐guided Monte Carlo method
International Journal for Numerical Methods in Fluids, 2011 AbstractIn this work we propose a new approach for the numerical simulation of kinetic equations through Monte Carlo schemes. We introduce a new technique that permits to reduce the variance of particle methods through a matching with a set of suitable macroscopic moment equations.
Degond P. +2 more
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