Comparison of Hypothesis Testing and Bayesian Model Selection

The main goal of both Bayesian model selection and classical hypotheses testing is to make inferences with respect to the state of affairs in a population of interest. The main differences between both approaches are the explicit use of prior information by Bayesians, and the explicit use of null distributions by the classicists. Formalization of prior information in prior distributions is often difficult. In this paper two practical approaches (encompassing priors and training data) to specify prior distributions will be presented. The computation of null distributions is relatively easy. However, as will be illustrated, a straightforward interpretation of the resulting p-values is not always easy. Bayesian model selection can be used to compute posterior probabilities for each of a number of competing models. This provides an alternative for the currently prevalent testing of hypotheses using p-values. Both approaches will be compared and illustrated using case studies. Each case study fits in the framework of the normal linear model, that is, analysis of variance and multiple regression.     

商用汽车离合器的选型及匹配计算

文章通过分析商用汽车离合器的结构形式及特点,介绍了在商用汽车的整车总布置中离合器的选型及匹配方法,为商用车离合器的设计选型匹配提供借鉴。     

供应商选择问题的模糊机会约束规划模型

论文以模糊机会约束规划的方法处理供应商选择问题（VSP）。在市场需求量为模糊变量,同时成本、质量、交付时间三个目标以一定的置信水平成立的前提下,提供了一种帮助决策者进行供应商供货量分配的方法,并给出模型的混合智能通用算法。     

On Model Selection Curves

Many popular methods of model selection involve minimizing a penalized function of the data (such as the maximized log-likelihood or the residual sum of squares) over a set of models. The penalty in the criterion function is controlled by a penalty multiplier λ which determines the properties of the procedure. In this paper, we first review model selection criteria of the simple form “Loss + Penalty” and then propose studying such model selection criteria as functions of the penalty multiplier. This approach can be interpreted as exploring the stability of model selection criteria through what we call model selection curves. It leads to new insights into model selection and new proposals on how to select models. We use the bootstrap to enhance the basic model selection curve and develop convenient numerical and graphical summaries of the results. The methodology is illustrated on two data sets and supported by a small simulation. We show that the new methodology can outperform methods such as AIC and BIC which correspond to single points on a model selection curve.     

拣货系统布局选择的决策模型与应用

文中提出拣货系统布局选择的决策模型,并给出了如何进行选择的决策方法:首先提出了15种布局策略,其次提出一种技术来衡量每种策略的绩效表现,再根据其实际表现择优。最后利用某财富500强的真实数据进行了验证。     

浅谈自动空气开关的选型

本文介绍了空气开关的作用、特点、原理、分类等,并根据用途的不同重点介绍了如何选择空气开关。     

基于鲍摩-瓦尔夫模型的配送中心选址

文中以总成本最低为目标,建立鲍摩——瓦尔夫模型,对物流配送中心进行选址。该模型在考虑运输成本的同时还考虑可变成本和固定成本,并用启发式算法对其进行就求解,最后通过具体的实例说明模型的可行性。     

天线选型在无线覆盖网络规划中的应用

良好的无线覆盖来自于网络规划的详细设计。无线覆盖规划是GSM网络规划的核心内容。本文重点介绍天线选型在无线覆盖的网络规划中的应用方案。     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.     

基于神经网络的供应商选择模型

对供应商选择方法进行了总结。论述了神经网络的基本原理，以及供应商选择的特点，在此基础上提出了基于神经网络的供应商选择模型，并总结了基于神经网络的供应商选择模型的优缺点。     

多供应商选择中双层规划模型应用研究

供应商在供应链中起着举足轻重的作用,做好供应商的选择对于核心企业来说至关重要。在当今市场环境中,对供应商的选择不仅要考虑总成本最低,同时还要考虑供应商的信用风险。因此可以建立双层模型对供应商的选择进行优化,其中上层模型(U)以成本最低为目标,从而可以选择出费用小的供应商;下层模型(L)以供应商的金融风险为标准进行筛选,引入层次分析法(AHP)得到供应商综合信用指数。再将上层选择出的供应商进行重新选择,从而确定供应商及采购数量。     

基于OWA算子的供应商选择模型研究

目前,基于AHP法构建的供应商选择模型中,均未考虑实际决策时通常有多人参与,并且决策者会给出不同形式评价信息的情况。针对这一问题,将供应商选择视为一种不同形式偏好信息并存的群决策问题,结合AHP法,提出基于OWA算子的供应商选择模型。     

Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

During the last years, graphical models have become a popular tool to represent dependencies among variables in many scientific areas. Typically, the objective is to discover dependence relationships that can be represented through a directed acyclic graph (DAG). The set of all conditional independencies encoded by a DAG determines its Markov property. In general, DAGs encoding the same conditional independencies are not distinguishable from observational data and can be collected into equivalence classes, each one represented by a chain graph called essential graph (EG). However, both the DAG and EG space grow super exponentially in the number of variables, and so, graph structural learning requires the adoption of Markov chain Monte Carlo (MCMC) techniques. In this paper, we review some recent results on Bayesian model selection of Gaussian DAG models under a unified framework. These results are based on closed-form expressions for the marginal likelihood of a DAG and EG structure, which is obtained from a few suitable assumptions on the prior for model parameters. We then introduce a general MCMC scheme that can be adopted both for model selection of DAGs and EGs together with a couple of applications on real data sets.     

基于模糊层次分析法的种粮机具选型管理

本文结合种粮机具选型的实际情况,提出从影响选型决策的基本因素出发来建立选型的指标体系,并讨论了种粮机具选型指标体系的构成。最后,将模糊层次分析法应用于种粮机具选型,建立了选型模型,并通过实例证明模型合理可行。     

低压熔断器的选用及注意事项

本文简要分析了熔断器和断路器的不同应用,重点介绍了熔断器的选型及使用注意事项。     

区间规划在证券投资组合问题中的运用

本文基于区间规划的方法研究了摩擦市场的投资组合选择问题。文中把风险证券的收益率、投资风险及证券的流动性用区间数来描述,并结合绝对偏差风险函数的思想建立了一种关于区间数的证券投资组合选择模型。最后利用区间数的两种序关系将所提出的模糊线性规划问题转化为普通的参数线性规划问题进而求其解。     

配送中心离散选址模型的研究

本文从同时考虑成本最小化和客户满意度的角度出发,介绍说明了配送中心选址离散决策的概念,并以此为基础建立了一个配送离散中心选址模型,同时用lingo软件为操作软件给出了一个运算实例.     

甘竹溪特大桥主墩塔吊选型及施工安装

本文介绍甘竹溪特大桥主塔、箱梁施工中起重设备塔吊的选型、安装及注意事项。     

基于CCSD模型的第三方物流供应商的评价与选择

首先设计了第三方物流供应商的评价指标体系;然后运用CCSD模型确定指标的客观权重,并分析了方案排序和最优方案保持不变的权重范围;最后进行实证研究,为企业选择第三方物流供应商提供了理论依据。     

基于商品性质的多级分销模式选择及网络库存定位模型的研究

本文以典型的多级分销网络为研究对象,从商品的需求量、需求波动及商品价值等特性对分销网络的服务能力和成本水平的影响出发,对如何根据不同商品特性选择科学的分销模式及如何在多级网络中进行合理的库存定位问题进行了研究,并基于此提出了一个可行的定性分析模型,借助此模型,可帮助企业在设计分销网络和现有网络的优化过程中,根据所经营商品的不同特性进行合理的网络设计和运营,大大提高网络运营效率。