Testing and Evaluation for Web Usability Based on Extended Markov Chain Model

As the increasing popularity and complexity of Web applications and the emergence of their new characteristics. the testing and maintenance of large, complex Web applications are becoming more complex and difficult. Web applications generally contain lots of pages and are used by enormous users. Statistical testing is an effective way of ensuring their quality. Web usage can be accurately described by Markov chain which has been proved to be an ideal model for software statistical testing. The results of unit testing can b eutilized in the latter stages, wbich is an important strategy for bottom-to-top integration testing, and the other improvement of extended Markov chain model (FMM) is to present the error type vector which is treated as a part of page node. This paper also proposes the algorithm for generating test cases of usage paths. Finally, optional usage reliability evaluation methods and an incremental usability regression testing model for testing and evaluation are presented.     

黑龙江省一次能源消费总量和消费结构预测研究

以黑龙江省一次能源为研究对象,选取其1995~2014年一次能源消费的历史数据,构建了改进的BP神经网络模型来预测黑龙江省2015~2020年一次能源的消费总量;重构马尔科夫模型,预测黑龙江省2015~2020年一次能源的消费结构。结果表明：构建的模型模拟预测结果误差小,预测准确度良好。2015~2020年黑龙江省一次能源消费总量基本稳定在9200万吨标准煤;能源消费结构中煤炭、石油和天然气的份额均有所降低,清洁能源占比呈增长趋势;到2020年,一次能源消费结构中煤炭占比仍高达65.39%,清洁能源占比仍处于弱势。建议：降低一次能源的消费总量,尤其是煤炭和石油在一次能源消费中的占比;合理调整一次能源的消费结构,降低煤炭在能源消费结构中的占比;政府出台相关政策,引导消费理念,优先使用清洁能源。     

基于马尔科夫生存模型与粒子群算法 的动态航路规划

针对未知情况下航路规划问题,采用动态规划策略保证飞机可以实时规划未来路径,并引入基于马尔科夫的生存模型来获取飞机的生存状态概率,从而评估生存代价,再综合任务、油耗、飞机机动性等作为粒子群算法的目标函数与约束条件,同时为了缓解生存与任务之间的矛盾,引入目标函数权重自适应策略.仿真实验证明,提出的动态航路规划策略是可行的,自适应权重也在一定程度上缓解了生存与任务之间的矛盾,同时将基于马尔科夫的生存模型应用于动态航路搜索中,能够更加直观地掌握每一时刻飞机的生存代价以及各状态的概率.     

基于Markov-vine copula的我国网贷平台对传统金融机构风险传染效应研究

近年来,网络借贷发展迅速,并且随着互联网技术的运用和网络借贷自身所蕴含的独特风险方式,使得其局部风险更容易蔓延至整个网贷市场,有可能会迅速波及,传染到其他类型的金融市场.本文将Markov区制转换模型和vine copula相结合,用来研究我国网络借贷平台对传统金融机构的动态风险传染效应.研究结果表明:我国网络借贷平台和相应的传统金融机构在不同阶段呈现出不同的"杠杆效应";我国网络借贷平台以及相关的传统金融机构均具有较为明显的区制转换特征;比较三个阶段的情况,网络借贷平台对商业银行的风险传染效应要大于其他的传统金融机构,其次是网络借贷平台对信托业的风险传染效应;在各阶段,网络借贷平台对传统金融机构的风险传染效应不对称,下尾相关系数基本大于上尾相关系数.     

基于目标导向的多属性决策属性价值公度方法

MACBETH方法在多个属性维度上确定出的一组属性Good值(或Neutral值)并不能真地具有保证属性价值公度的完全相同的偏好涵义.为克服该方法缺陷,基于包容性属性价值函数,通过引入数据包络分析技术和马尔可夫理论给出了以目标参照方案为导向的目标导向属性价值函数和多属性决策属性价值公度的方法程序.相比于具有较大随意性确定属性Good值和Neutral值的MACBETH方法,依赖目标导向属性价值函数的多属性决策属性价值公度程序具有更明显的科学合理性,因此具有发展MACBETH方法,推动实际决策应用该方法及相关多属性偏好关联决策模型的创新价值.基于决策实验的应用分析直接验证了多属性决策属性价值公度方法程序的科学有效性,间接验证了包容性属性价值函数和目标导向属性价值函数的科学理性.     

基于逆向强化学习的装船时堆场翻箱智能决策

集装箱码头装船时堆场翻箱具有时序性与动态性，属于NP（non?deterministic polynomial）难问题。针对常见的顺岸式集装箱码头堆场，以最小化总翻箱次数为优化目标，考虑翻箱对装船连续性及效率的影响，基于马尔科夫决策过程构建装船时堆场翻箱模型，设计逆向强化学习算法。为验证算法的有效性，以随机决策为基准，将设计的逆向强化学习算法与码头常见规则决策、随机决策对比。结果表明，贝位堆存状态不佳时，常见的规则决策不一定优于随机决策；逆向强化学习算法可有效挖掘隐含专家经验，收敛至最小翻箱次数的概率更高，且不同堆存状态下均能更好地限制单次发箱的翻箱次数，可实现装船时堆场翻箱智能决策。     

Curie’s principle and causal graphs

Curie’s Principle says that any symmetry property of a cause must be found in its effect. In this article, I consider Curie’s Principle from the point of view of graphical causal models, and demonstrate that, under one definition of a symmetry transformation, the causal modeling framework does not require anything like Curie’s Principle to be true. On another definition of a symmetry transformation, the graphical causal modeling formalism does imply a version of Curie’s Principle. These results yield a better understanding of the logical landscape with respect to the relationship between Curie’s Principle and graphical causal modeling.     

具有不可比状态信息的可修MS-PMS可靠性分析

针对复杂多阶段任务系统(PMS)中存在的元件状态多态性和不可比性问题,提出了系统与元件两层的分层模块构建方法,应用连续时间Markov模型(CTMC)描述可修元件之间状态的相依和变迁的动态行为,应用多态多值决策图(MVDD)构建系统的结构函数.为处理元件状态不可比情形,将其分为组内、组间及无约束状态三组,应用CTMC构建了不同变迁约束下部件处于不同阶段时的状态组的联合概率模型,发展了具有不可比元件状态的多状态多阶段任务系统(MSPMS)可靠性分析方法.最后,通过简约形式的飞行任务系统实例,结果表明CTMC下分层分组模块分析方法能有效计算MS-PMS的可靠度,验证了模型与分析方法的可行性.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Multicategory Purchase Incidence Models for Partitions of Product Categories

We analyze multicategory purchases of households by means of heterogeneous multivariate probit models that relate to partitions formed from a total of 25 product categories. We investigate both prior and post hoc partitions. We search model structures by a stochastic algorithm and estimate models by Markov chain Monte Carlo simulation. The best model in terms of cross‐validated log‐likelihood refers to a post hoc partition with two groups; the second‐best model considers all categories as one group. Among prior partitions with at least two category groups a five‐group model performs best. Effects on average basket value differ for the model with five prior category groups from those for the best‐performing model in 40% and 24% of the investigated categories for features and displays, respectively. In addition, the model with five prior category groups also underestimates total sales revenue across all categories by about 28%. Copyright © 2016 John Wiley & Sons, Ltd.