Over-parameterisation is a key issue in (integrated) environmental modelling. Therefore, a sensitivity analysis (SA) can assist in the proper application of a complex environmental model, such as the Soil and Water Assessment Tool (SWAT). A very powerful sensitivity analysis technique that is gaining popularity in environmental modelling is the variance-based Sobol’ method. Still, the high numb...

The splitting of Quasi-Monte Carlo (QMC) point sequences into blocks or interleaved substreams has been suggested to raise the speed of distributed numerical integration and to lower to traffic on the network. The usefulness of this approach in GRID environments is discussed. After specifying requirements for using QMC techniques in GRID environments in general we review and evaluate the propos...

A method for generating sequences of quasirandom numbers allows conventional serial Monte Carlo algorithms to be parallelized with no loss of computation eeciency. Speciically, a Sobol' sequence can be broken up into interleaved subsets; with each processing node calculating a unique subset of the full sequence, all of the computational advantages of quasirandom Monte Carlo methods over pure-ra...

Sensitivity analysis involves determining the contribution of individual input factors to uncertainty in model predictions. The most commonly used approach when doing a sensitivity analysis on spatial models is using Monte Carlo simulation. There are a number of techniques for calculating sensitivity indices from the Monte Carlo simulations, some more effective or efficient than others. These t...

Monte Carlo methods are used extensively in computational finance to estimate the price of financial derivative options. In this paper we review the use of quasi-Monte Carlo methods to obtain the same accuracy at a much lower computational cost, and focus on these key ingredients: i) the generation of Sobol and lattice points; ii) reduction of effective dimension using the principal component a...

In the context of multi-factor stochastic volatility models, which contain the widely used Heston model, we present variance reduction techniques to price European options by Monte Carlo (MC) and QuasiMonte Carlo (QMC) methods. We formulate a stochastic integral as a martingale control for the payoffs to be evaluated. That control corresponds to the cost of an approximate delta hedging strategy...

Fitting a probability distribution to observed or generated data constitutes an essential part of any data analysis system. The Generalized Lambda Distribution, while extremely versatile in this regard, is also a difficult distribution to fit. Parameter estimation methods that attempt to match moments or quantiles of the data require minimizing a bivariate non-linear function. The suitability o...

In this paper we study the possibility to use the Sobol’ and Halton quasi-random number sequences (QRNs) in solving the BarkerFerry (B-F) equation which accounts for the quantum character of the electron-phonon interaction in semiconductors. The quasi-Monte Carlo (QMC) solutions obtained by QRNs are compared with the Monte Carlo (MC) solutions in case when the scalable parallel random number ge...