Monte Carlo simulation is named after the city of Monte Carlo in Monaco, which is famous for gambling such as roulette, dice, and slot machines. Since the simulation process involves generating chance variables and exhibits random behaviors, it has been called Monte Carlo simulation. Monte Carlo simulation is a powerful statistical analysis tool and widely used in both non-engineering fields an...

A new approach to cluster simulation is developed in the context of nucleation theory. This approach is free of any arbitrariness involved in the definition of a cluster. Instead, it preferentially and automatically generates the physical clusters, defined as the density fluctuations that lead to nucleation, and determines their equilibrium distribution in a single simulation, thereby completel...

the length of equal minimal and maximal blocks has eected on logarithm-scale logarithm against sequential function on variance and bias of de-trended uctuation analysis, by using quasi monte carlo(qmc) simulation and cholesky decompositions, minimal block couple and maximal are founded which are minimum the summation of mean error square in horest power.

The sensor location information is necessary for the application in wireless sensor network. It is useful to position the unknown node by studying the movement mode of anchor node in the network. This paper proposed a Monte Carlo based localization algorithm. Simulation results showed that the proposed algorithm decreases the node location error of prediction.

A cellular engineer typically estimates system performance via simulation. Most cellular operations software provides data from which one can infer the average, busy hour, subscriber location distribution, which becomes an input to the simulation. When the simulation does not include mobility, as is typical with Monte Carlo simulations, modeling this distribution is a straight-forward task. How...

We have explored and tested the behavior of Monte-Carlo Search Algorithms in both artificial and real game trees. Complementing the work of previous WPI students, we have expanded the Gomba Testing Framework; a platform for the comparative evaluation of search algorithms in large adversarial game trees. We implemented and analyzed the specific UCT algorithm PoolRAVE by developing and testing va...

A simulation based method for the numerical solution of PDEs with random coefficients is presented. By the Feynman-Kac formula, the solution can be represented as conditional expectation of a functional of a corresponding stochastic differential equation driven by independent noise. A time discretization of the SDE for a set of points in the domain and a subsequent Monte Carlo regression lead t...

In this paper the computational complexity of the marginalized particle filter is analyzed and a general method to perform this analysis is given. The key is the introduction of the equivalent flop measure. In an extensive Monte Carlo simulation different computational aspects are studied and compared with the derived theoretical results.