Computational analysis and modelling of wave propagation and interaction with objects are essential to help understand the fundamental problems of wave travelling. Though various methods have been proposed to model the wave propagation procedures, Monte Carlo simulation plays important role as an effective method to trace individual phone history, based on the statistics of large photon populat...

In this paper we apply a Monte-Carlo Tree Search implementation that is boosted with domain knowledge to the game of poker. More specifically, we integrate an opponent model in the Monte-Carlo Tree Search algorithm to produce a strong poker playing program. Opponent models allow the search algorithm to focus on relevant parts of the game-tree. We use an opponent modelling approach that starts f...

This paper compares fault position and Monte Carlo methods as the most common methods in stochastic assessment of voltage sags. To compare their abilities, symmetrical and unsymmetrical faults with different probability distribution of fault positions along the lines are applied in a test system. The voltage sag magnitude in different nodes of test system is calculated. The problem with the...

In this paper we show how to derive the mean and variance of the transilluminated signal obtained in models of light propagation in tissue, based both on a stochastic Monte Carlo method and on a deterministic diffusion approximation. The theoretical treatment of the Monte Carlo model applies only to integrated intensity measurements, whereas the diffusion approximation gives an estimator for th...

Computation using Monte Carlo simulations is widely used for modelling the light-tissue interaction. Despite this, many of the methods used for building such simulations are poorly described in the literature. In particular, a scheme for translating the scatter angles produced from a phase function into updated photon direction vectors is not explicitly reported. To address this, a method for c...

We present a new localization algorithm called Sensor Resetting Localization which is an extension of Monte Carlo Localization. The algorithm adds sensor based resampling to Monte Carlo Localization when the robot is lost. The new algorithm is robust to modelling errors including unmodelled movements and systematic errors. The algorithm can be used in real time on systems with limited computati...

This note investigates long-run exclusion in a cointegrated vector autoregressive (VAR) model from the viewpoint of …nite-sample statistical inference. Monte Carlo experiments show that, in various circumstances, a mis-speci…ed partial VAR model, which is justi…ed by the existence of a long-run excluded variable, can lead to better …nite-sample inference for cointegrating rank than a fully-spec...

background: palladium-103 (103pd) is a brachytherapy source for cancer treatment. the monte carlo codes are usually applied for dose distribution and effect of shieldings. monte carlo calculation of dose distribution in water phantom due to a med3633 103pd source is presented in this work. materials and methods: the dose distribution around the 103pd model med3633 located in the center of 30×30...

This work derives and simulates two choice models applying the weighted utility theory, a generalization of the expected utility theory. It shows one set of assumptions, which justify the practice of including the mean and the variance of a risky alternative into a linear utility function of the choice model. A Monte Carlo simulation provides empirical evidence on the robustness of the models.

High-temperature gas-cooled reactor fuels are composed of thousands of microscopic fuel particles, randomly dispersed in a graphite matrix. The modelling of such geometry is complicated, especially using continuous-energy Monte Carlo codes, which are unable to apply any deterministic corrections in the calculation. This paper presents the geometry routine developed for modelling randomly disper...