this paper proposes a hybrid method to find cumulative distribution function (cdf) of completion time of gert-type networks (gtn) which have no loop and have only exclusive-or nodes. proposed method is cre-ated by combining an analytical transformation with gaussian quadrature formula. also the combined crude monte carlo simulation and combined conditional monte carlo simulation are developed a...

introduction: in recent decades, several monte carlo codes have been introduced for research and medical applications. these methods provide both accurate and detailed calculation of particle transport from linear accelerators. the main drawback of monte carlo techniques is the extremely long computing time that is required in order to obtain a dose distribution with good statistical accuracy. ...

medical linear accelerators are one of the most widespread methods for cancer treatment. despite their advantages, unwanted photoneutrons are produced by high energy linacs. this photoneutrons are as undesired doses to patients and a significant problem for radiation protection of the staffs and patients. photoneutrons radiological risk must be evaluated because of their high let and range.in o...

Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used rejuvenate particles. We discuss how calibrate automatically (using current particles) Hamiltonian within SMC. To do so, we build upon adaptive SMC approach of Fearnhead and Taylor (2013), also suggest methods. illustrate advantages us...

Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~^), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Ca...

This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy. Finally through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-Monte Carlo methods to hi...

Background: Monte Carlo (MC) modeling of a linear accelerator is a prerequisite for Monte Carlo dose calculations in external beam radiotherapy. In this study, a simple and efficient model was developed for Elekta SL-25 linear accelerator using MCNP4C Monte Carlo code Materials and methods: The head of Elekta SL-25 linac was simulated for 6 and 18 MV photon beams using MCNP4C MC code. Energ...

monte carlo simulation is a standard technique for project risk analysis. however, some assumptions of this technique are not reasonable in real world. when a project falls behind the schedule, managers take actions to improve the performance. nevertheless, most of the simulation models as monte carlo omit these reactions in their analysis, which results in unreasonable wide distributions or ev...

In this paper, we deal with the pricing of power options when the dynamics of the risky underling asset follows the double stochastic volatility with double jump model. We prove efficiency of our considered model by fast Fourier transform method, Monte Carlo simulation and numerical results using power call options i.e. Monte Carlo simulation and numerical results show that the fast Fourier tra...