This study proposes a new method to quantitatively represent the uncertainty existing in open pit optimization results due to variations in mineral prices. After generating multiple mineral prices using Monte Carlo simulation with data on past mineral prices, a probability model that represents the uncertainty was developed by integrating multiple open pit optimization results derived from the ...

A figure of merit is proposed in order to optimize the self-organized growth of nanoscale elements into one-/two-dimensional arrays via a fine selection of the deposition/annealing conditions. This figure of merit has been designed to account for the most significant defects inherent in such arrays. Its versatility has been studied by kinetic Monte Carlo simulations of self-organized growth of ...

A novel method to improve the yield gradient estimation in parametric yield optimization is proposed. By introducing some deterministic information into the conventional Monte Carlo method and fully utilizing the samples, it is possible to obtain yield gradient estimation with significantly smaller variance. The additional computation is almost negligible. Examples are presented to indicate the...

To determine the size of an extensive air shower it is not necessary to have knowledge of the function that describes the fall-off of signal size from the shower core (the lateral distribution function). In this paper an analysis with a simple Monte Carlo model is used to show that an optimum ground parameter can be identified for each individual shower. At this optimal core distance, r opt , t...

We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. 2004 , an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asympto...

Number-theoretic rules are particularly suited for the approximation of multi-dimensional integrals in which the integrands are periodic. When the integrands are not periodic, then a vertex-modiied variant has been proposed. An error bound for such vertex-modiied rules is based on a simple generalization of the L2 discrepancy. In s dimensions these vertex-modiied rules contain 2 s weights which...

We propose a variant of the simulated annealing method for optimization in the multivhriate analysis of differentiable functions. The method uses global actualizations via the hybrid Monte Carlo algorithm in their generalized version for the proposal of new configurations. We show how this choice can improve upon the performance of simulated annealing methods (mainly when the number of variable...

Observations of Type Ia supernovae used to map the expansion history of the Universe suffer from systematic uncertainties that need to be propagated into the estimates of cosmological parameters. We propose an iterative Monte Carlo simulation and cosmology fitting technique (SMOCK) to investigate the impact of sources of error upon fits of the dark energy equation of state. This approach is esp...

This advanced tutorial aims at an exposition of problems in finance that are worthy of study by the Monte Carlo research community. It describes problems in valuing and hedging securities, risk management, portfolio optimization, and model calibration. It surveys some areas of active research in efficient procedures for simulation in finance and addresses the impact of the business context on t...

Perfect Information Monte Carlo (PIMC) search is a practical technique for playing imperfect information games that are too large to be optimally solved. Although PIMC search has been criticized in the past for its theoretical deficiencies, in practice it has often produced strong results in a variety of domains. In this paper, we set out to resolve this discrepancy. The contributions of the pa...