This program uses Markowitz portofolio theory to find combination of stocks in a portfolio which has minimum Variance for expected returns
标签： combination portofolio Markowitz portfolio
上传时间： 2017-05-07
上传用户：李彦东
使用INTEL矢量统计类库的程序,包括以下功能: Raw and central moments up to 4th order Kurtosis and Skewness Variation Coefficient Quantiles and Order Statistics Minimum and Maximum Variance-CoVariance/Correlation matrix Pooled/Group Variance-CoVariance/Correlation Matrix and Mean Partial Variance-CoVariance/Correlation matrix Robust Estimators for Variance-CoVariance Matrix and Mean in presence of outliers
标签： 61623 and Kurtosis central
上传时间： 2017-05-14
上传用户：yzy6007
This file is a function under matlab which allow to compute several statistical parameter of random signal such as Variance, power, mean values, std, ...
标签： statistical parameter function compute
上传时间： 2017-06-27
上传用户：ruixue198909
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance objectives. Such optimization or tuning of the virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the Variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
标签： Optimization and Uncertainty Quantification
上传时间： 2016-04-08
上传用户：huhu123456
压缩包中有5篇论文，分别为《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》这是一篇博士论文，较为详细的介绍了各种EDA算法；《Anisotropic adaptive Variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二阶混合矩的高斯分布估计算法》介绍了一些基于EDA的创新算法。
上传时间： 2020-05-25
上传用户：duwenhao
