俄韩军事技术合作及其战略问题的研究

俄韩军事技术合作发端于"以武器还债务",可随着国际格局和地区形势的发展变化,对韩军事技术合作日益成为俄在东北亚地区实现本国政治、经济、外交与安全利益的重要战略手段。不过,俄韩军事技术合作的快速发展也给朝鲜半岛局势带来一定的负面影响和消极作用,对此,中国要有所认识和考量。当前俄韩军事技术合作层次较高、领域广泛、潜力较大,但受美国与朝鲜等外部因素的影响,两国军事技术合作的进一步深化与发展仍面临一系列的困难和挑战。     

An online Bayesian mixture labelling method by minimizing deviance of classification probabilities to reference labels

Solving label switching is crucial for interpreting the results of fitting Bayesian mixture models. The label switching originates from the invariance of posterior distribution to permutation of component labels. As a result, the component labels in Markov chain simulation may switch to another equivalent permutation, and the marginal posterior distribution associated with all labels may be similar and useless for inferring quantities relating to each individual component. In this article, we propose a new simple labelling method by minimizing the deviance of the class probabilities to a fixed reference labels. The reference labels can be chosen before running Markov chain Monte Carlo (MCMC) using optimization methods, such as expectation-maximization algorithms, and therefore the new labelling method can be implemented by an online algorithm, which can reduce the storage requirements and save much computation time. Using the Acid data set and Galaxy data set, we demonstrate the success of the proposed labelling method for removing the labelling switching in the raw MCMC samples.     

语码转换的功能分析

运用系统功能语言学的理论分析语码转换现象的人际功能和语篇功能。语码转换作为一种特殊的交际策略,在使用中有着多种微妙的人际功能,并且能起到诸如引起话题、衔接连贯、突出焦点信息等功能。     

A Simple Solution to Bayesian Mixture Labeling

The label-switching problem is one of the fundamental problems in Bayesian mixture analysis. Using all the Markov chain Monte Carlo samples as the initials for the expectation-maximization (EM) algorithm, we propose to label the samples based on the modes they converge to. Our method is based on the assumption that the samples converged to the same mode have the same labels. If a relative noninformative prior is used or the sample size is large, the posterior will be close to the likelihood and then the posterior modes can be located approximately by the EM algorithm for mixture likelihood, without assuming the availability of the closed form of the posterior. In order to speed up the computation of this labeling method, we also propose to first cluster the samples by K-means with a large number of clusters K. Then, by assuming that the samples within each cluster have the same labels, we only need to find one converged mode for each cluster. Using a Monte Carlo simulation study and a real dataset, we demonstrate the success of our new method in dealing with the label-switching problem.     

Optimal Forecasts from Markov Switching Models

We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the construction of optimal weights takes uncertainty around states into account. Performance of the optimal weights is shown through simulations and an application to U.S. GNP, where using optimal weights leads to significant reductions in MSFE. Supplementary materials for this article are available online.     

现代建筑中的荒诞艺术

本文就现代建筑中荒诞艺术表现的形态、意图和界限进行了系列的探讨 ,用以扩大审美视野 ,激活和诱导当代建筑创作。     

REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO METHODS AND SEGMENTATION ALGORITHMS IN HIDDEN MARKOV MODELS

We consider hidden Markov models with an unknown number of regimes for the segmentation of the pixel intensities of digital images that consist of a small set of colours. New reversible jump Markov chain Monte Carlo algorithms to estimate both the dimension and the unknown parameters of the model are introduced. Parameters are updated by random walk Metropolis–Hastings moves, without updating the sequence of the hidden Markov chain. The segmentation (i.e. the estimation of the hidden regimes) is a further aim and is performed by means of a number of competing algorithms. We apply our Bayesian inference and segmentation tools to digital images, which are linearized through the Peano–Hilbert scan, and perform experiments and comparisons on both synthetic images and a real brain magnetic resonance image.     

营业税改征增值税：必要性、难点、影响的探讨

长期以来,我国的营业税与增值税是并行征收的,随着我国经济的发展,营业税重复征税、增值税链条不完整、降低第三产业国际竞争力等弊端日益显现。因此,对营业税改征增值税的必要性、难点及其影响的探讨尤为重要。     

社区超市购买意愿驱动因素研究

随着我国城市化进程的加快,社区超市也取得了快速的发展。在与大型超市等零售业态竞争的过程中,如何增强消费者的购买意愿成为社区能否持续发展的关键。文章从实证的角度分析了消费者购买意愿的驱动因素。研究认为,经营社区超市的企业可以从改善超市氛围、提升员工服务、统一品牌店铺形象等方面增强消费者的购买意愿,以此来提升社区超市竞争力。     

基于Markov机制转换模型的黄金价格周期波动研究

自20世纪70年代以来,黄金价格波动日益频繁。基于Markov机制转换模型对1975-2011年间黄金价格波动的特征进行分析,发现黄金价格波动在不同的机制（上涨、震荡状态、下跌）间周期转换。各种机制的平均持续时间不同,其中黄金价格上涨或者下跌的平均持续期是19周左右,黄金价格维持震荡状态的平均持续期约为24周,持续时间较长。另外,黄金价格波动所处的波动机制由诸多因素引起,但最重要的因素是由当时的世界经济状况,尤其是美国经济状况所决定的。