This paper considers a Monte-Carlo Nystrom method for solving integral equations of the second kind, whereby values $(z(y_i))_{1\leq i\leq N}$ solution $z$ at set $N$ random and independent points $(y_i)_{1\leq are approximated by $(z_{N,i})_{1\leq discrete $N$-dimensional linear system obtained replacing with empirical average over samples N}$. Under unique assumption that equation admits $z(y...

Event-by-event intermittency analysis of Toy Monte Carlo events is performed in the scenario high multiplicity as case at recent colliders RHIC and LHC for AA collisions. A power law behaviour Normalized Factorial Moments (NFM), F_{q} Fq function number bin...

In this paper we investigate the so called foresight bias that may appear in the Monte-Carlo pricing of Bermudan and compound options if the exercise criteria is calculated by the same Monte-Carlo simulation as the exercise values. The standard approach to remove the foresight bias is to use two independent Monte-Carlo simulations: One simulation is used to estimate the exercise criteria (as a ...

due to the expected increase of defects in circuits based on deep submicron technologies, reliability has become an important design criterion. although different approaches have been developed to estimate reliability in digital circuits and some measuring concepts have been separately presented to reveal the quality of analog circuit reliability in the literature, there is a gap to estimate re...

Introduction: Monte Carlo calculation method is considered to be the most accurate method for dose calculation in radiotherapy. The purpose of this research is comparison between 6 MV Primus LINAC simulation output with commissioning data using EGSnrc and build a Monte Carlo geometry of 6 MV Primus LINAC as realistically as possible. The BEAMnrc and DOSXYZnrc (EGSnrc package) M...

A review of Monte Carlo methods for approximating the high-dimensional integrals that arise in Bayesian statistical analysis. Emphasis is on the features of many Bayesian applications which make Monte Carlo methods especially appropriate, and on Monte Carlo variance-reduction techniques especially well suited to Bayesian applications. A generalized logistic regression example is used to illustr...