供应链管理中的信息流网络

本文对供应链管理中的信息流理论进行了探讨 ,指出了传统链式信息流模式的缺陷 ,分析了简单的网络式信息流模型的构造 ;阐述了信息流网络的特征与优势 ;对若干有待深入研究的问题进行了展望。     

Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.     

Bayesian analysis of directed graphs data with applications to social networks

Summary.  A fully Bayesian analysis of directed graphs, with particular emphasis on applica- tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed.     

Optimal sampling for repeated binary measurements

The authors consider the optimal design of sampling schedules for binary sequence data. They propose an approach which allows a variety of goals to be reflected in the utility function by including deterministic sampling cost, a term related to prediction, and if relevant, a term related to learning about a treatment effect To this end, they use a nonparametric probability model relying on a minimal number of assumptions. They show how their assumption of partial exchangeability for the binary sequence of data allows the sampling distribution to be written as a mixture of homogeneous Markov chains of order k. The implementation follows the approach of Quintana & Müller (2004), which uses a Dirichlet process prior for the mixture.     

谁开创了贝叶斯学派？——对拉普拉斯1774年一篇文章的回顾

1764年,贝叶斯的遗作《机遇理论中一个问题的解》，是当今统计学界贝叶斯学派的开山之作，然而直到20世纪，贝叶斯的文章却并未对后世贝叶斯学派的发展产生重大影响。事实上，真正促使贝叶斯的思想在科学界得到广泛传播的关键人物是大科学家拉普拉斯。1774年，拉普拉斯发表的关于贝叶斯理论的里程碑式的文章，使现代读者充分认识到拉普拉斯对贝叶斯学派发展所起的巨大作用，并从拉普拉斯的思想中获得了有益的新知。     

Bayesian analysis of elapsed times in continuous‐time Markov chains

The authors consider Bayesian analysis for continuous‐time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software.     

New methods for fitting multiple sinusoids from irregularly sampled data

A novel framework is proposed for the estimation of multiple sinusoids from irregularly sampled time series. This spectral analysis problem is addressed as an under-determined inverse problem, where the spectrum is discretized on an arbitrarily thin frequency grid. As we focus on line spectra estimation, the solution must be sparse, i.e. the amplitude of the spectrum must be zero almost everywhere. Such prior information is taken into account within the Bayesian framework. Two models are used to account for the prior sparseness of the solution, namely a Laplace prior and a Bernoulli–Gaussian prior, associated to optimization and stochastic sampling algorithms, respectively. Such approaches are efficient alternatives to usual sequential prewhitening methods, especially in case of strong sampling aliases perturbating the Fourier spectrum. Both methods should be intensively tested on real data sets by physicists.     

A strategy for third-party logistics systems: A case analysis using the blue ocean strategy

One of today's most frequently discussed topics in the business world is how to escape from the intense Red Ocean and how to create an uncontested Blue Ocean. However, because there are few practical guidelines available on this topic, we will introduce a case study of a third-party logistics (3PL) provider, CJ-Global Logistics Service (CJ-GLS), to show how it aspires to be a leader in the newly introduced 3PL industry in South Korea. CJ-GLS is a latecomer in the logistics industry, and its resources, such as the number of trucks and warehouses, are relatively small in comparison to those of established companies. But, it has achieved a distinct competitive advantage through innovative information technology (i.e., RFID—radio frequency identification), which has enabled it to create an uncontested market space, electronic logistics business. One remarkable fact about CJ-GLS is that its swift growth comes not from attracting competitors’ customers from the existing Red Ocean market but from creating a Blue Ocean market (3PL market), which previously existing incumbents ignored, and also from constructing a new business model founded on a RFID-based, ubiquitous-oriented 3PL system. Analyzed through a Four Actions Framework and characterized as Blue Ocean, this case study provides valuable information on how a company reinforces its competitive advantage from the Red Ocean while it transitions into a Blue Ocean by utilizing advanced information communication technologies.     

The evaluation of DNA evidence in pedigrees requiring population inference

Summary. The evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.