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About 3,903 papers for “Latin hypercube sampling”

Latin Hypercube Sampling

Jaroslav Menčík · Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) · 2016

The simultaneous influence of several random quantities can be studied by the Latin hypercube sampling method (LHS). The values of distribution functions of each quantity are distributed uniformly in the interval (0; 1) and these values of all variables are randomly combined. This method yields statistical characteristics with less simulation experiments than the Monte Carlo method. In this chapter, the creation of the randomized input values is explained.

Cited by 0Open access

The Latin hypercube sampling.

Honghua Liu (15307022), Zhiliang Huang (1589509), Lei Wei (18880) et al. · Figshare · 2023

<div><p>During mild moxibustion treatment, uncertainties are involved in the operating parameters, such as the moxa-burning temperature, the moxa stick sizes, the stick-to-skin distance, and the skin moisture content. It results in fluctuations in skin surface temperature during mild moxibustion. Existing mild moxibustion treatments almost ignore the uncertainty of operating parameters. The uncertainties lead to excessive skin surface temperature causing intense pain, or over-low temperature reducing efficacy. Therefore, the interval model was employed to measure the uncertainty of the operation parameters in mild moxibustion, and the uncertainty optimization design was performed for the operation parameters. It aimed to provide the maximum thermal penetration of mild moxibustion to enhance efficacy while meeting the surface temperature requirements. The interval uncertainty optimization can fully consider the operating parameter uncertainties to ensure optimal thermal penetration and avoid patient discomfort caused by excessive skin surface temperature. To reduce the computational burden of the optimization solution, a high-precision surrogate model was established through a radial basis neural network (RBNN), and a nonlinear interval model for mild moxibustion treatment was formulated. By introducing the reliability-based possibility degree of interval (RPDI), the interval uncertainty optimization was transformed into a deterministic optimization problem, solved by the genetic algorithm. The results showed that this method could significantly improve the thermal penetration of mild moxibustion while meeting the skin surface temperature requirements, thereby enhancing efficacy.</p></div>

Cited by 0Open access

Conditioned Latin Hypercube Sampling

Pierre Roudier, Lazyload Yes, Needscompilation No · 2013

Description This package implements the conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006). This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012). Depends R (> = 2.9.0) Imports utils, methods, grid, ggplot2, sp, raster, reshape2, plyr,scales

Cited by 0

clhs: Conditioned Latin Hypercube Sampling

Pierre Roudier · 2012

Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) &lt;<a href="https://doi.org/10.1016%2Fj.cageo.2005.12.009" target="_top">doi:10.1016/j.cageo.2005.12.009</a>&gt;. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, &lt;<a href="https://doi.org/10.1201%2Fb12728" target="_top">doi:10.1201/b12728</a>&gt;).

Cited by 43Open access

aclhs: Autocorrelated Conditioned Latin Hypercube Sampling

Van Huong Le, Rodrigo Vargas · 2025

Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), &lt;<a href="https://doi.org/10.1016%2Fj.cageo.2005.12.009" target="_top">doi:10.1016/j.cageo.2005.12.009</a>&gt;. For acLHS, see Le and Vargas (2024) &lt;<a href="https://doi.org/10.1016%2Fj.cageo.2024.105539" target="_top">doi:10.1016/j.cageo.2024.105539</a>&gt;.

Cited by 0Open access

clhs-package Conditioned Latin Hypercube Sampling

Pierre Roudier, Lazyload Yes, Needscompilation No · 2014

Description This package implements the conditioned Latin hypercube sampling, as published by Mi-nasny and McBratney (2006). This method proposes to stratify sampling in presence of ancil-lary data. An extension of this method, which propose to associate a cost to each individ-ual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012). Depends R (&amp;gt; = 2.14.0) Imports utils, methods, grid, ggplot2, sp, raster, reshape2, plyr,scales

Cited by 0

Quantile Estimation with Latin Hypercube Sampling

Hui Dong, Marvin K. Nakayama · Operations Research · 2017

Quantiles are often used to measure risk of stochastic systems. We examine quantile estimators obtained using simulation with Latin hypercube sampling (LHS), a variance-reduction technique that efficiently extends stratified sampling to higher dimensions and produces negatively correlated outputs. We consider single-sample LHS (ssLHS), which minimizes the variance that can be obtained from LHS, and also replicated LHS (rLHS). We develop a consistent estimator of the asymptotic variance of the ssLHS quantile estimator’s central limit theorem, enabling us to provide the first confidence interval (CI) for a quantile when applying ssLHS. For rLHS, we construct CIs using batching and sectioning. On average, our rLHS CIs are shorter than previous rLHS CIs and only slightly wider than the ssLHS CI. We establish the asymptotic validity of the CIs by first proving that the quantile estimators satisfy Bahadur representations, which show that the quantile estimators can be approximated by linear transformations of estimators of the cumulative distribution function. We present numerical results comparing the various CIs.

Cited by 41

Descriptive Sampling: An Improvement Over Latin Hypercube Sampling

Eduardo Saliby Coppead, Descriptive Sampling (ds, A Monte Carlo Sampling · 2007

Descriptive Sampling (DS), a Monte Carlo sampling technique based on a deterministic selection of the input values and their random permutation, represents a deep conceptual change on how to carry out a Monte Carlo application. Abandoning the paradigm that a random selection of sample values would be necessary in order to describe random behavior, DS is a rather polemical idea. An interesting issue related to DS are the similarities between it and Latin Hypercube Sampling (LHS) to be discussed in this paper. After a brief description of both methods, it is shown how close DS and LHS are. As such, DS can be seen as a limiting case of LHS and also as an improvement over it. An experiment and a set of empirical results illustrating the relationship between DS and LHS are also presented.

Cited by 0

Latin hypercube sampling with inequality constraints

Matthieu Petelet, Bertrand Iooss, Olivier Asserin et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2009

In some studies requiring predictive and CPU-time consuming numerical models, the sampling design of the model input variables has to be chosen with caution. For this purpose, Latin hypercube sampling has a long history and has shown its robustness capabilities. In this paper we propose and discuss a new algorithm to build a Latin hypercube sample (LHS) taking into account inequality constraints between the sampled variables. This technique, called constrained Latin hypercube sampling (cLHS), consists in doing permutations on an initial LHS to honor the desired monotonic constraints. The relevance of this approach is shown on a real example concerning the numerical welding simulation, where the inequality constraints are caused by the physical decreasing of some material properties in function of the temperature.

Cited by 0Open access

Latin Hypercube Sampling in Sensitivity Analysis

Joseph C. Collins, III - · 1994

Abstract : In practice, production computer simulation models are usually run for a limited number of real input vectors. To make statistical inferences about the model, it is frequently desirable to generate additional realistic inputs for the purpose of obtaining better coverage of the input space. Latin Hypercube Sampling is presented as a method for accomplishing this task. Experimental design, Matrices(mathematics).

Cited by 1

Schematic diagram of Latin hypercube sampling.

Guilin Yang (583364), Liya Yu (404045) · Figshare · 2024

&lt;div&gt;&lt;p&gt;To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. Introducing the Latin hypercube method to construct the initial population so that the individuals of the initial population are evenly distributed in the solution space, increasing the diversity of the initial population. Introducing nonlinear convergence factors based on positive cut functions to changing the convergence of algorithms, the early survey capabilities and later development capabilities of the algorithm are balanced. The manta ray foraging strategy is introduced at the position update to make up for the defect that the algorithm is prone to local optimization, which effectively improves the optimization performance of the algorithm. To evaluate the performance of the proposed algorithm, 27 well-known test reference functions were selected for experimentation, which showed significant advantages compared to other algorithms. Finally, in order to further verify the algorithm’s applicability in actual production processes, it was applied to solve scheduling problems in three flexible workshop scenarios and an aviation engine job shop scheduling in an enterprise. This confirmed its efficacy in addressing complex real-world problems.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access