A recently developed Centroidal Voronoi Tessellation (CVT) sampling method is investigated here to assess its suitability for use in response surface generation. CVT is an unstructured sampling method that can generate nearly uniform point spacing over arbitrarily shaped Mdimensional parameter spaces. For rectangular parameter spaces (hypercubes), CVT appears to extend to higher dimensions more...

Manufactured parts differ from ideal shape. Therefore tolerances are used in product development in order to constrain the admissible deviations. The impact of tolerances is analyzed in different ways. It is common to employ Monte Carlo Sampling in order to obtain a statistical result by the iterative evaluation of the functional relationship. In contrast to this, Robust Design has established ...

This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for...

The variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing m...

In previous work by Domingo-Ferrer et al., rank swapping and multivariate microaggregation has been identified as well-performing masking methods for microdata protection. Recently, Dandekar et al. proposed using synthetic microdata, as an option, in place of original data by using Latin hypercube sampling (LHS) technique. The LHS method focuses on mimicking univariate as well as multivariate s...

Sensitivity analysis is a key part of a comprehensive energy simulation study. Monte-Carlo techniques have been successfully applied to many simulation tools. Several sampling techniques have been proposed in the literature; however to date there has been no comparison of their performance for typical building simulation applications. This paper examines the performance of simple random, strati...

In this work we augment a known Monte Carlo simulationbased approach to stochastic discrete optimization problem, the so called Sample Average Approximation (SAA) method, with a new criterion to decide when the search has to be stopped. Our approach exploits a well known and effective sampling technique, Latin Hypercube Sampling (LHS), and confidence interval analysis, a well established approx...

A procedure for extending the size of a Latin hypercube sample (LHS) with rank correlated variables is described and illustrated. The extension procedure starts with an LHS of size m and associated rank correlation matrix C and constructs a new LHS of size 2m that contains the elements of the original LHS and has a rank correlation matrix that is close to the original rank correlation matrix C....

Efficient sampling strategies that scale with the size of the problem, computational budget, and users’ needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, called Progressive Latin Hypercube Sampling (PLHS), which sequentially generates sample points while progressively preserving the distributional pro...

We propose a discrete model to investigate the role of contact tracing program reducing the newcases and prevalence of tuberculosis. We observe that the tuberculosis contact tracing program has no effect on the basic reproduction number R0 but the size of social cluster has. On the other hand a contact tracing program can speed up the process of TB elimination. We compute the partial rank corre...