基于UG的视图创建过程研究

在使用UG创建二维图时,会用到大量视图来表达不同零件间的关系。熟练掌握UG软件中常见视图的特点、使用技巧及其之间的区别,可提高工作效率,创造价值。     

流动妇女收入影响的DAG图模型研究

基于江苏省流动妇女生存状况的实地调查,运用贝叶斯网络构造流动妇女收入影响因素的DAG图模型。与线性模型结果不同,流动妇女个体特征并不都直接影响月收入,其中年龄对教育程度和婚姻状况有直接影响,来源地对教育程度有直接影响,教育程度和婚姻状况对职业类型选择有直接影响,职业类型对月收入有直接影响,年龄、来源地、教育程度和婚姻状况对月收入有间接影响。     

长株潭一体化公路网构架优化的探讨

为加快长株潭一体化公路网的建设进程,降低公路网建设的成本,对长株潭一体化公路网结构进行了研究,将长株潭城市群十五大片区组团构成的公路网分布情况,抽象为一个赋权连通图,在此基础上应用Prim算法,得出了相应赋权连通图的最小支撑树,进而提出了长株潭一体化公路网构架建设的可行性方案,通过比较分析,说明了方案的有效性与合理性.     

Seeing and Doing: the Concept of Causation

This note is an extended review of the book by Judea Pearl (2000) on causality, in which the basic concepts therein are explained in a form that statisticians will hopefully appreciate, including some comments on their relevance to inference and decision-making.     

Statistical analysis of change in networks

A survey is given of random graphs and random graph processes which can be used to describe and analyze networks that are changing with time. Marko-vian change over time, log-linear models for change, and conditionally uniform models for change are described. It is noted that estimation is usually complex if the random graph involves dependent dyads. Models with deterministic change over time may be a way to avoid the difficulties implied by dependent dyads. Logit regression methods are described that can be used to estimate such models.     

一种基于图论求解多端口网络Y参数矩阵的方法

对于端口数较多、较复杂的网络，据定义直接计算Y参数矩阵将十分麻烦，甚至不可能，该文提出一种基于图论、由关联矩阵及导纳矩阵直接计算Y参数矩阵的方法，并推导出其具体的求解公式。     

Bounds for Unknown Elements of Input–Output Matrices

For unknown elements of input–output matrices, strict lower and upper bounds are computed using the column and row sums of the matrix. Furthermore, matrix elements are identified that are only virtually unknown. Also, it it discussed how additional information will affect the set of possible values for the unknown elements.     

RANDOM WALKS ON REGULAR POLYHEDRA AND OTHER DISTANCE–REGULAR GRAPHS

In this paper we consider Markov chains of the following type: the state space is the set of vertices of a connected, regular graph, and for each vertex transitions are to the adjacent vertices, with equal probabilities. When the mean first–passage matrix F of such a Markov chain is symmetric, the expectation and variance of first–entrance times, recurrence times, number of visits to a vertex and the expectation of the number of different vertices visited, can easily be computed from the entries of F. The method is most effective, when the underlying graph is distance–regular; then F is symmetric and the entries of F can easily be obtained from the graph.     

Influence Diagrams for Causal Modelling and Inference

We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decision, etc ., we obtain a generalisation of influence diagrams that supports meaningful causal modelling and inference, and only requires concepts and methods that are already standard in the purely probabilistic case. We relate our representations to others, particularly functional models, and present arguments and examples in favour of their superiority.     

Monitoring the Behaviour of Credit Card Holders with Graphical Chain Models

Consumer credit has become an enormous business in industrialized countries. Recently, finance agencies have started to develop new products aiming not only to widen their portfolio but also to keep active relationships with good clients already taken on file and to prevent bad clients from becoming a loss for the agency. As a result, models for the whole behaviour of the clients are necessary. They involve many related outcome variables, which altogether give a measure of whether the client revealed to be profitable or unprofitable. This paper aims to show the potential of graphical chain models in the described context.