A statistical investigation of the effects of random mistuning on the forced response of nearly cyclic assemblies made of mono-coupled, single-mode component systems (blades) is presented. Various probabilistic methods of analysis are applied to generate the statistics of the forced response: an analytical first-order statistical perturbation method, a purely numerical Monte Carlo simulation, a...

We present a lattice Monte Carlo simulation for the evaluation of the spectrum of a confining Yang-Mills theory without Goldstone boson. We show that this theory is a very good candidate for describing composite weak bosons. In order to perform the spectrum analysis we have used standard lattice QCD Monte Carlo methods. We have also developed an efficient method to evaluate the mass of the pseu...

While the Quasi-Monte Carlo method of numerical integration achieves smaller integration error than standard Monte Carlo, its use in particle physics phenomenology has been hindered by the abscence of a reliable way to estimate that error. The standard Monte Carlo error estimator relies on the assumption that the points are generated independently of each other and, therefore, fails to account ...

In this paper, we apply a mixed Monte Carlo and Quasi-Monte Carlo method, which we proposed in a previous paper, to problems from mathematical finance. We estimate by simulation the Up-and-Out barrier options and Double Knock-Out barrier options. We assume that the stock price of the underlaying asset S = S(t) is driven by a Lévy process L(t). We compare our estimates with the estimates obtaine...

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence of the method when the number of copies increases. We illustrate the method with numerical examples w...

We propose a simulation method for state space models which utilizes quasi-Monte Carlo method with randomization aiming at good approximation of the probability distribution at each time step. Numerical experiments shows our proposed method outperforms a traditional Monte Carlo simulation method in the estimation of model likelihood.

This paper proposes a Monte Carlo approach for the evaluation of integrals of smooth functions defined on compact Lie groups. The approach is based on the ergodic property of Brownian processes in compact Lie groups. The paper provides an elementary proof of this property and obtains the following results. It gives the rate of almost sure convergence of time averages along with a “large deviati...

In this paper, a new activity-based method for the concurrent assessment and tracing of costs, energy consumption and waste generation for usage in Life-Cycle Design is presented. The method is based on Activity-Based Costing (ABC). By utilizing ABC we are able to assess and trace accurately overhead and direct costs, energy and waste generation.. The inherent uncertainty is modeled in terms of...

Group sequential tests are widely used for interim analyses in randomized clinical trials. These tests have been extended to bivariate outcomes, but calculation of boundary values is computationally intensive. In this paper, we compare 3 algorithms: (1) quasi-Monte Carlo; (2) Schervish’s MULNOR; and (3) a bivariate version of Armitage’s recursive formula proposed by Jennison and Turnbull, for c...

This paper presents a novel method for the discovery of new analytical filters suitable for filtering of noise in Monte Carlo rendering. Our method utilizes genetic programming to evolve the set of analytical filtering expressions with the goal to minimize image error in training scenes. We show that genetic programming is capable of learning new filtering expressions with quality comparable to...