Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.     

神性、人性和理性--略论艺术形态与时代精神

艺术的本质在于其精神性,从艺术和时代精神的相互关系中去确立划分艺术形态的标准是符合艺术本质的.按照艺术所包含的精神内容的不同,艺术形态可分为神性艺术、人性艺术和理性艺术,以丰富的艺术史例可以说明其特征.     

A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

反腐败的国内立法问题研究

当前腐败问题的滋生蔓延,与法律规范真空太多、腐败分子有空子可钻有很大的关系。反腐败面临的重重困难,迫切需要有专门的反腐败法律和法律体系的支持。通过借鉴国外和中国古代的立法经验,从如何建立反腐败的法律体系、如何完善反腐败的法律条款等方面提出了一些见解。     

文学思潮中的“花花公子”——简论“欲望化写作”

欲望化写作是当今文坛上方兴未艾的且引人注目的文学现象,其注重张扬感官放纵和感官感受。本文拟从欲望化写作的成因、分类以及欲望化写作的积极影响和消极影响等几个方面来探讨欲望化思潮。     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

冗余度机器人的运动规划碰撞算法

在考虑了操作机的关节极限、自碰撞和静态障碍物的情况下,从给定的初始位形出发,发现一条到末端效应器目标位置和姿态的相连可到达路径。方法给出了机器人操作机点到点逆运动学问题求解算法,利用碰撞算法实现了冗余度机器人运动规划,仿真验证了该方法的有效性,并表明了该方法具有较大的实用价值。     

动静态属性数据相结合的客户分类方法研究

研究了一种客户动态、静态属性数据相结合的客户分类方法。提出了客户时间序列的加权处理方法,并应用客户时间序列的统计特征作为聚类特征向量,采用混合式遗传算法对客户聚类,使每一类客户具有相似的时序特征。在此基础上将聚类结果与客户的静态属性数据相结合,对客户进一步分类。实验结果表明,与传统的基于静态属性数据的客户分类方法相比,本文的方法提高了客户分类的准确性。