Sequential learning versus no learning in Bayesian regression models

We consider two regression models: linear and logistic. The dependent variable is observed periodically and in each period a Bayesian formulation is used to generate updated forecasts of the dependent variable as new data is observed. One would expect that including new data in the Bayesian updates results in improved forecasts over not including the new data. Our findings indicate that this is not always true. We show there exists a subset of the independent variable space that we call the “region of no learning.” If the independent variable values for a given period in the future are in this region, then the forecast does not change with any new data. Moreover, if the independent variable values are in a neighborhood of the region of no learning, then there may be little benefit to wait for the new data and update the forecast. We propose a statistical approach to characterize this neighborhood which we call the “region of little learning.” Our results provide insights into the trade‐offs that exist in situations when the decision maker has an incentive to make an early decision based on an early forecast versus waiting to make a later decision based on an updated forecast. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 532–548, 2014     

SDI,NATO, and the Social Composition Function

The strategic defense initiative (SDI) intends to renew the leadership of the USA on the western alliance. The initiative takes place in a period when a summation technology prevails for the aggregation of contributions of NATO allies. We investigate if SDI induces a shift in Hirshleifer’s social composition function. Panel data tests over the period 1970–1990 do not confirm any break toward a best-shot aggregator. SDI does not alter the core of deterrence. It is indeed a public good at the US level but not at the NATO level, where, it is one of the joint products of the alliance. We also investigate the lessons to be drawn for the current debates on ballistic defense.     

陆军作战仿真模型组件化设计

陆军作战仿真是一个复杂的系统工程,仿真所涉及的模型种类繁多、类别不一。针对当前陆军作战仿真模型开发不规范、通用性与可重用性差、开发和集成效率低的现状,在对陆军作战仿真模型构成要素进行分析和归纳的基础上,提出仿真模型组件化设计的建模方法、采用"方案-单元-实体-组件"方式的模型管理机制以及采用事件驱动、事件订购和分发方式的模型交互机制。     

一类具有饱和发生率的离散型SIS传染病模型的全局渐近稳定性

研究一类具有饱和发生率的离散型SIS传染病模型,得到模型的基本再生数.通过比较原理以及构造适当的Lyapunov函数,证明当基本再生数R01时,无病平衡点是全局渐近稳定的;当基本再生数R01时,地方病平衡点是全局渐近稳定的.     

试论汉语学习对英语学习者翻译能力的重要性

语言和文化密不可分,因此语言的学习也是文化的学习。一种文化的存在离不开交流,而文化交流离不开翻译和翻译工作者。合格的译者至少要精通两种语言。翻译工作者几乎都来自外语学习者,因此,研究英语学习者的汉语水平与其翻译能力的关系,有助于对症下药,为将来的翻译工作打好基础。     

具有时滞和变消耗率的比率型Chemostat模型稳定性分析

研究了一类具有时滞和变消耗率的比率型Chemostat模型,讨论了边界平衡点和正平衡点的局部稳定性以及Hopf分支的存在性;利用比较原理和构造Lyapunov泛函,给出了边界平衡点和正平衡点的全局稳定性的充分性条件;并通过数值模拟验证了理论结果。     

一类二次Hamilton系统在三次多项式扰动下的极限环

通过计算一类二次Hamilton系统的高阶Mel′nikov函数,对该系统在三次多项式扰动下分岔的极限环个数进行了估计。     

基于近似策略的联合优化方法

针对优化中收敛速度和优化解全局性的问题,提出了一种联合优化方法:构造原问题的近似模型,使用全局优化方法对近似函数进行优化,得到优化点作为初值,再使用局部优化方法对原问题进行优化.为了获得对原问题更好的近似,改进了径向基插值方法,以优化误差的方法来选择参数.利用临近空间机翼模型的优化对算法进行了测试,结果表明,优化参数的...     

军校《高等数学》教学存在的问题及对策

针对新的军事变革对军校《高等数学》教学所提出的新要求,分析了当前军队本科院校《高等数学》教学面临的现状及亟待解决的问题。针对问题提出了四种解决方法,并提出了教员在教学活动中实施创新、提高学员积极性应注意的四个方面。为了提高《高等数学》的教学效果,需要教员在教学中从多方面入手,提高学员的学习积极性,为学员创设一种轻松、愉快,富有好奇心、求知欲,充满自信的课堂环境。