A new decision to take for cost-sensitive Naïve Bayes classifiers

Practical classification problems often involve some kind of trade-off between the decisions a classifier may take. Indeed, it may be the case that decisions are not equally good or costly; therefore, it is important for the classifier to be able to predict the risk associated with each classification decision. Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. The objective is to quantify the trade-off between various classification decisions using probability and the costs that accompany such decisions. Within this framework, a loss function measures the rates of the costs and the risk in taking one decision over another.     

Researchers Propose AFM-based Virtual Nano-hand for Stable Nanomanipulation

<正>The atomic force microscopy(AFM)is a promising tool for manipulating nano-objects to fabricate nano-structures or nano-devices.However,there are still some challenges facing the development of an AFM-based robotic nanomanipulation system,such as the     

一类二元非乘积型Meyer—konig—Zeller概率算子的饱和定理

借助二元抛物线引理,探讨一类二元非乘积型Meyer—konigandZeller概率算子的饱和性,得到了一个点态饱和定理．     

随机P-集合及其随机特征

在P-集合概念的基础上,根据元素迁移的随机性,提出随机P-集合的概念,给出了P-集合的随机生成,P-集合的强随机生成.并根据元素迁移的概率,讨论了随机P-集合的随机特征,给出了随机P-集合的存在背景及在风险投资决策估计中的应用.     

农村居民收入增长趋势下家电需求模型的实证研究——以四川省为例

通过实证研究四川农村居民收入水平增长的状况,分析了农村居民边际消费倾向的变化趋势,构建了基于农村家电市场需求的概率模型,阐述了扩大内需,促进农村家电消费市场可持续发展的思路。     

一种寻求初始p-有效点的随机模拟方法

利用随机模拟方法结合试探的策略给出了一种求解初始p-有效点的方法，并给出了随机变量分别为离散型、连续型的示例．数值结果表明，此算法能够找出符合要求的初始p-有效点.     

概率赋范空间的乘积空间及拓扑

本定义了有限个概率线赋范空间的乘积空间，它仍是一个概率线性赋范空间。并证明了乘积空间中由概率范数导出的拓扑与乘积拓扑的等价性。本将概率线性赋范空间简称PN空间。     

一种基于概率的覆盖粗糙集模型研究

文章给出了一种基于概率覆盖的上、下近似的定义,并且研究了它的性质、数字特征、拓扑特征等,为研究覆盖粗糙集提供了新的模型.     

How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects

Evaluative bibliometrics is concerned with comparing research units by using statistical procedures. According to Williams (2012) an empirical study should be concerned with the substantive and practical significance of the findings as well as the sign and statistical significance of effects. In this study we will explain what adjusted predictions and marginal effects are and how useful they are for institutional evaluative bibliometrics. As an illustration, we will calculate a regression model using publications (and citation data) produced by four universities in German-speaking countries from 1980 to 2010. We will show how these predictions and effects can be estimated and plotted, and how this makes it far easier to get a practical feel for the substantive meaning of results in evaluative bibliometric studies. An added benefit of this approach is that it makes it far easier to explain results obtained via sophisticated statistical techniques to a broader and sometimes non-technical audience. We will focus particularly on Average Adjusted Predictions (AAPs), Average Marginal Effects (AMEs), Adjusted Predictions at Representative Values (APRVs) and Marginal Effects at Representative Values (MERVs).     

A Probabilistic Model for Evaluation of Volleyball Scoring Systems

Abstract

A probabilistic model for the game of volleyball is developed to evaluate alternative scoring systems. This Markov chain model is then recursively solved, to determine the distribution of the number of serves per game and the probability that a given team wins. These results are then used to evaluate the effects on the game of volleyball of a proposed change in the scoring system.