Calculation of Phenomenological Coefficients by Monte Carlo Computer Simulation Methods

Stochastic Programming by Monte Carlo Simulation Methods

We consider in this paper stochastic programming problems which can be formulated as an optimization problem of an expected value function subject to deterministic constraints. We discuss a Monte Carlo simulation approach based on sample average approximations to a numerical solution of such problems. In particular, we give a survey of a statistical inference of the sample average estimators of...

Methods of Monte Carlo Simulation

Monte-Carlo methods for Computer Vision

In figure 1, suppose we are throwing darts to each of the Cartesian systems. The probability of a dart falling anywhere in the red area is easy to compute when we are dealing with analytical shapes such as a rectangle or a circle; but how do we compute the probability of success in the third case? One approach is the Riemann integration method by separating the whole system into a thin grid and...

Quasi - Monte Carlo Methods in Computer Graphics

The problem of global illumination in computer graphics is described by a Fredholm integral equation of the second kind. Due to the complexity of this equation, Monte Carlo methods provide an efficient tool for the estimation of the solution. A new approach, using quasi-Monte Carlo integration, is introduced and compared to Monte Carlo integration. We discuss some theoretical aspects and give n...

Sequential Monte Carlo Methods in Computer Vision