converges to E(f(U)) almost surely when N tends to infinity. This suggests a very simple algorithm to approximate I: call a random number generator N times and compute the average (??). Observe that the method converges for any integrable function on [0, 1] : f is not necessarily a smooth function. In order to efficiently use the above Monte-Carlo method, we need to know its rate of convergence...

performing a probabilistic study rather than a determinist one is a relatively easy way to quantify the uncertainty in an engineering design. due to the complexity and poor accuracy of the statistical moment methods, the monte carlo simulation (mcs) method is wildly used in an engineering design. in this work, an mcs-based reliability analysis was carried out for the stability of the chain pill...

New regulations, stronger competitions and more volatile capital markets have increased the demand for stochastic asset-liability management (ALM) models for insurance companies in recent years. The numerical simulation of such models is usually performed by Monte Carlo methods which suffer from a slow and erratic convergence, though. As alternatives to Monte Carlo simulation, we propose and in...

Abstract We introduce the use of Zig-Zag sampler to problem sampling conditional diffusion processes (diffusion bridges). The is a rejection-free scheme based on non-reversible continuous piecewise deterministic Markov process. Similar Lévy–Ciesielski construction Brownian motion, we expand path in truncated Faber–Schauder basis. coefficients within basis are sampled using sampler. A key innova...

In this paper, the Diffusion Limited Cluster Aggregation (DLCA) method is employed to reconstruct the three-dimensional network of silica aerogel. Then, simulation of nitrogen adsorption at 77 K in silica aerogel is conducted by the Grand Canonical Monte Carlo (GCMC) method. To reduce the computational cost and guarantee accuracy, a continuous-discrete hybrid potential model, as well as an adso...

This paper presents the reliability analysis of three benchmark problems using three variants of Subset Simulation. The original version of Subset Simulation, SubSim/MCMC, employs a Markov chain Monte Carlo (MCMC) method to simulate samples conditional on intermediate failure events; it is a general method that is applicable to all the benchmark problems. SubSim/Splitting is a variant of Subset...

Monte Carlo simulation is a popular tool for reliability assessment because of its robustness and ease of implementation. However, the computational cost is a major concern. The standard deviation of the estimated failure probability of a system from N replications is proportional to 2 / 1  N . Efforts to reduce computational cost focus on development of intelligent sampling procedures and eff...

In this study, a Monte Carlo simulation (MCs)-based isogeometric stochastic Finite Element Method (FEM) is proposed for uncertainty quantification in the vibration analysis of piezoelectric materials. method, deterministic solutions (natural frequencies) coupled eigenvalue problem are obtained via (IGA). Moreover, MCs employed to solve various parameters, including separate elastic and constant...

Four robust estimators of multivariate locations are compared in a Monte Carlo study by examining their empirical efficiency performance on a range of bivariate distributions with heavier tails than the Gaussian distribution. Using three different multivariate measures of relative efficiency, the simulation results show that the Hodges-Lehmann estimator is "safest" among the robust estimators, ...