[27]. The sampling method technique used in Latin hypercube is sampling without any replacement. In a sense, the number of stratifications done by the sequence is equal This independence is done by selecting in
Latin hypercube sampling (LHS) uses a stratified sampling scheme to improve on the coverage of the k-dimensional input space for such computer models. This means that a single sample will provide useful information when some input variable(s) dominate certain responses (or certain time intervals)...
拉丁超立方体抽样Latin hypercube sampling说明[转] LifeBug:03.随机变量和3F(PDF、CDF、PMF)
How to use Latin hypercube sampling for structural related problems? can be explained with a example?팔로우 조회 수: 1 (최근 30일) Abhiram 2014년 9월 9일 추천 0 링크 번역 For a beam problem, d=F*L^3/(3*E*I) ...
As an example, we mention the work in [127], which compares D- and I-optimal designs with space-filling designs such as Latin hypercube, uniform, sphere packing, and maximum entropy, in terms of reducing the prediction variance for high-order polynomials. It was shown that D- and I-...
X = lhsnorm(mu,sigma,n) returns a numeric matrix X containing a Latin hypercube sample of size n from a multivariate normal distribution with mean vector mu and covariance matrix sigma. The size of X is n-by-d, where d is the size of mu. X is similar to a random sample generated ...
拉丁超立方采样(Latin Hypercube Sampling, LHS)是一种统计采样技术,用于生成一组样本点,这些样本点在参数空间中均匀分布,并且满足每个维度只有一个样本点落在该维度的取值范围内。LHS常用于参数优化和模型验证等领域。在Python中,有几种常用的包可以进行拉丁超立方采样,下面介绍两种常用的方法。1. 使用pyDOE2包pyDOE...
探索未知的科学殿堂，拉丁超立方采样（Latin Hypercube Sampling, LHS）与蒙特卡洛模拟如同夜空中的璀璨星辰，照亮了处理不确定性问题的迷雾。蒙特卡洛，这个名字本身就蕴含着一种随机的魔力，它通过海量的模拟实验，为我们揭示概率世界的奥秘，哪怕是看似简单的抛硬币游戏，也能借此估算出五次投掷中出现一次...
拉丁超立方采样是一种特殊的采样方法，用于改进蒙特卡洛模拟中的随机抽样。它通过将样本空间分层，然后在每层内随机抽样，最后将样本打乱顺序，得到结果。相比于随机抽样，拉丁超立方采样在复杂分布场景下的效率更高，能够更均匀地覆盖样本空间，尤其是在正态分布等复杂分布的情况下。拉丁超立方采样的主要优点...
(2019) Gan, Valdez. Metamodeling for Variable Annuities. This is sampling utility implementing Latin hypercube sampling from multivariate normal, uniform & empirical distribution. Correlation among variables can be sprecified.