ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

论物权法之区分原则

物权行为的独立性决定了区分原则的必然存在.区分原则,不是物权变动与原因行为的区分,而是引起物权变动的物权行为与原因行为即债权行为的区分.区分原则无论在理论或实践上均有极其重要的意义,因而在即将通过的物权法中,应确立区分原则的重要地位.     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

自动控制专业英语的教学改革探索

专业英语在选材上要重视先进性 ,在编排上力求系统性 ,注意贯穿自动化专业的主要专业课 ,如电力电子技术、模糊控制、神经网络、离散控制、微机控制技术、PLC、过程自动化、现代控制理论、楼宇自动化与智能大厦等 ,使这门课程不仅是对专业课程的总结 ,而且又是一种补充。同时增加专业英语知识 ,以加强对学生专业英语技能的培养 ,提高全面素质。具体教学中要明确教学目的 ,内容和方式 ,让学生自主选择。     

A Unified Approach to the Characterization of Equivalence Classes of DAGs, Chain Graphs with no Flags and Chain Graphs

Abstract.  A Markov property associates a set of conditional independencies to a graph. Two alternative Markov properties are available for chain graphs (CGs), the Lauritzen–Wermuth–Frydenberg (LWF) and the Andersson–Madigan– Perlman (AMP) Markov properties, which are different in general but coincide for the subclass of CGs with no flags . Markov equivalence induces a partition of the class of CGs into equivalence classes and every equivalence class contains a, possibly empty, subclass of CGs with no flags itself containing a, possibly empty, subclass of directed acyclic graphs (DAGs). LWF-Markov equivalence classes of CGs can be naturally characterized by means of the so-called largest CGs , whereas a graphical characterization of equivalence classes of DAGs is provided by the essential graphs . In this paper, we show the existence of largest CGs with no flags that provide a natural characterization of equivalence classes of CGs of this kind, with respect to both the LWF- and the AMP-Markov properties. We propose a procedure for the construction of the largest CGs, the largest CGs with no flags and the essential graphs, thereby providing a unified approach to the problem. As by-products we obtain a characterization of graphs that are largest CGs with no flags and an alternative characterization of graphs which are largest CGs. Furthermore, a known characterization of the essential graphs is shown to be a special case of our more general framework. The three graphical characterizations have a common structure: they use two versions of a locally verifiable graphical rule. Moreover, in case of DAGs, an immediate comparison of three characterizing graphs is possible.     

随机变量投影的一种应用

利用随机变量的投影关系,定义了偏方差矩阵,从而导出了逆方差阵元素的一种形式在随机变量是正态的条件,它为判别条件独立性有方便的操作办法。     

PC看门狗的原理与实现

根据单片机防止程序飞出的原理，创新性的提出了PC机在无人看管下，计算机如何判断当前的运行状态，以及在无法相应其他进程时，实现自动重启的方法。从单片机的看门狗实现原理，可在PC机上实现类似看门狗的功能。介绍了PC WATHDOG的软硬件实现，以及如何设计串口通讯协议，使PC WATCHDOG通过串口接收发送通讯帧和控制帧，进行交互，达到看门狗的功能。     

论当代中国司法独立的价值定位

全球化不仅对当代中国经济、政治、文化、社会生活等方面带来巨大的影响,而且也对中国法治建设提出了严峻挑战,其中至关重要的一个领域就是对司法独立制度建设方面的挑战。面对波澜壮阔的法律全球化浪潮,我们应该从中国的国情出发,在现行宪政的体制下,完善我国的司法独立制度,而不是全盘接受西方司法独立的政治体制及其所谓西方法律理想图景并根据它们去设计中国的法制未来。     

农产品电商集群企业地理邻近性对协同创新绩效的影响

基于知识治理视角,构建农产品电商集群企业地理邻近性、知识治理、知识共享和协同创新绩效关系的链式中介模型,并引入网络关系强度作为调节变量,结合341家农产品电商集群企业的调研数据,采用多层次回归和Bootstrap分析方法,分析地理邻近性对协同创新绩效的直接作用,检验知识治理和知识共享的独立中介作用和链式中介作用,以及网络关系强度的调节作用。结果表明：地理邻近性正向影响协同创新绩效;知识治理在地理邻近性和协同创新绩效间发挥中介作用;知识共享在知识治理和协同创新绩效间发挥中介作用;知识治理和知识共享在地理邻近性和协同创新绩效间发挥链式中介作用;网络关系强度正向调节正式知识治理和显性知识共享的链式中介作用。因此,农产品电商集群企业要把握数字经济发展契机,善于利用集群区位优势和产业资源,激发正式知识治理和非正式知识治理的双重效应,与合作企业建立良好的知识共享关系,实现高质量协同创新发展。     

Curved Chaotic Map Time Series Models and Their Stochastic Reversals

This paper considers two types of chaotic map time series models, including the well-known tent, logistic and binary-shift maps as special cases; these are called curved tent and curved binary families. Deterministic behaviour is investigated by invariant distributions, Lyapunov exponents, and by serial dependency. Stochastic time reversal of the families is shown to produce models which have a broader range of stochastic and chaotic properties than their deterministic counterparts. The marginal distributions may have concentrations and restricted supports and are shown to be a non-standard class of invariant distribution. Dependenc y is generally weaker with the reversed stochastic models. The work gives a broad statistical account of deterministic and stochastically reversed map models, such as are emerging in random number generation, communica tion systems and cryptography