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pro vyhledávání: '"William J. Morokoff"'
Autor:
William J. Morokoff
Publikováno v:
Applied Mathematical Finance. 6:19-28
A proof of convergence is presented for a simplified numerical integration method for solving systems of correlated stochastic differential equations describing mean reverting geometric Brownian motion. Such systems arise in modelling the time evolut
Autor:
Alfred Kersch, William J. Morokoff
Computer simulation of semiconductor processing equipment and devices requires the use of a wide variety of numerical methods. Of these methods, the Monte Carlo approach is perhaps most fundamentally suited to mod eling physical events occurring on
Publikováno v:
SIAM Journal on Numerical Analysis. 35:2117-2127
The compressible Euler equations possess a family of generalized entropy densities of the form $\rho f(\sigma)$, where $\rho$ is the mass density, $\sigma$ is the specific entropy, and f is an arbitrary function. Entropy inequalities associated with
Publikováno v:
Physics of Fluids. 10:3214-3226
We present criteria for monitoring the validity of the Navier–Stokes approximation during the simulation of a rarefied gas. Our approach is based on an underlying kinetic formulation through which one can construct nondimensional non-negative defin
Autor:
William J. Morokoff
Publikováno v:
SIAM Review. 40:765-788
The need to simulate stochastic processes numerically arises in many fields. Frequently this is done by discretizing the process into small time steps and applying pseudorandom sequences to simulate the randomness. This paper addresses the question o
Autor:
William J. Morokoff
Publikováno v:
The Journal of Computational Finance. 2:75-100
Publikováno v:
The Journal of Computational Finance. 1:27-46
Publikováno v:
Journal of Computational Physics. 122:218-230
The standard Monte Carlo approach to evaluating multidimensional integrals using (pseudo)-random integration nodes is frequently used when quadrature methods are too difficult or expensive to implement. As an alternative to the random methods, it has
Publikováno v:
SIAM Journal on Scientific Computing. 15:1251-1279
Quasi-random (also called low discrepancy) sequences are a deterministic alternative to random sequences for use in Monte Carlo methods, such as integration and particle simulations of transport processes. The error in uniformity for such a sequence
Publikováno v:
SIAM Journal on Numerical Analysis. 30:1558-1573
The convergence of the Monte Carlo method for numerical integration can often be improved by replacing random numbers with more uniformly distributed numbers known as quasi-random. In this paper the convergence of Monte Carlo particle simulation is s