信息熵公理及其在决策分析中的应用

针对信息量是消息发生前的不确定性给出一个直观测量信息量公式.为了克服Shannon熵的局限性和分析信息度量本质,借鉴距离空间理论中度量公理定义的思路,通过非负性、对称性、次可加和极大性给出信息熵的公理化新定义.将Shannon熵、直观信息熵和β-熵等不同形式的信息度量统一在同一公理化结构下.应用直观信息熵公式仅采用四则运算进行决策树分析,避免了利用Shannon熵公式的对数运算.     

区别度诱导的广义模糊熵

广义模糊熵是模糊熵在广义模糊补意义下的推广,本文从区别度的角度给出几个生成广义模糊熵的途径;通过一个具体的区别度公式得到了相应的一些广义模糊熵表达式,为实际使用广义模糊熵做了一些理论上的铺垫.     

模糊事件贝叶斯公式

讨论了模糊事件的概率及其基本性质 ,并通过对经典贝叶斯公式的推广 ,提出了模糊事件的贝叶斯公式 .     

全概率公式的推广

首先给出了普通事件在普通条件和Fuzzy条件下的Fuzzy条件概率及Fuzzy事件在普通条件和Fuzzy条件下的Fuzzy条件概率公式,并通过对普通事件的全概率公式进行推广,得到普通事件和Fuzzy事件分别在普通划分和Fuzzy划分下的全概率公式     

全概率公式的推广及其在保险中的应用

在实际应用中,可以利用随机变量的联合分布、条件分布及边缘分布推导出全概率公式,其基本思想是将一个边缘密度分解成条件密度,使所要解决的问题简化.通过具体实例,分析条件概率和全概率公式在保险中的广泛应用.     

计算数学期望和概率的条件化方法

借助于条件数学期望和随机事件A的示性函数IA,通过对随机变量的适当"条件化"处理,应用全期望公式和推广的全概率公式,讨论了计算数学期望和概率的条件化方法.     

全概率公式的推广与应用

认为全概率公式成立的条件＂事件组须为样本空间的划分＂可以减弱,给出全概率公式在有限事件组情形和无限可列事件组情形下的两种推广形式,由此对贝叶斯公式进行两种相应推广,并通过实例展示全概率公式在敏感性调查中的应用.     

独立重复试验概率公式的全面阐述

高中数学教科书第二册 (下B)P1 32“独立重复试验”一节的概率公式 ,要作深入理解和全面阐述 ,否则学生处理这类问题时容易程式化 ,硬套公式 ,条件稍作变化便不知所措 .1　独立重复试验的概率公式有一定的局限性1 .1　概念的理解一般地讲 ,独立重复试验应符合三个条件 :①任两次试验之间是相互独立的 ;②每一次试验都有两个事件 ,且这两个事件是相互对立的 ;③每次试验中的每个事件发生的概率是相同的 .这是判定是否为独立重复试验的三个条件 .在判定一个概率问题是独立重复试验问题后 ,我们再用其公式求概率 .1 .2　公式Pn(k) =CknPk( 1 …     

约束矩阵方程的解的表示及行列式公式

本文利用Ｂｏｔｔ－Ｄｕｆｆｉｎ逆导出约束矩阵方程的唯一解或通解的表示及其较为简洁的行列式公式，并由之给出了某些特殊矩阵方程与约束性方程组的解以及几个重要广义逆矩阵的表示及其较是洁的行列式公式，我们的结果改进与推广了文（２－１１，１４，１５）中的有关结果。     

全概率公式的推广及其应用

全概率公式及其思想在概率统计与随机过程中具有重要作用.给出了公式在条件概率下的推广形式与具体应用.同时,得到了独立性条件下的特殊形式.     

Logic and probabilistic systems

Following some ideas of Roberto Magari, we propose trial and error probabilistic functions, i.e. probability measures on the sentences of arithmetic that evolve in time by trial and error. The set of the sentences that get limit probability 1 is a theory, in fact can be a complete set. We prove incompleteness results for this setting, by showing for instance that for every there are true sentences that get limit probability less than . No set as above can contain the set of all true sentences, although we exhibit some containing all the true sentences. We also consider an approach based on the notions of inner probability and outer probability, and we compare this approach with the one based on trial and error probabilistic functions. Although the two approaches are shown to be different, we single out an important case in which they are equivalent. Received March 20, 1995     

The reduction and fusion of fuzzy covering systems based on the evidence theory

This paper studies reduction of a fuzzy covering and fusion of multi-fuzzy covering systems based on the evidence theory and rough set theory. A novel pair of belief and plausibility functions is defined by employing a method of non-classical probability model and the approximation operators of a fuzzy covering. Then we study the reduction of a fuzzy covering based on the functions we presented. In the case of multiple information sources, we present a method of information fusion for multi-fuzzy covering systems, by which objects can be well classified in a fuzzy covering decision system. Finally, by using the method of maximum flow, we discuss under what conditions, fuzzy covering approximation operators can be induced by a fuzzy belief structure.     

An anytime deduction algorithm for the probabilistic logic and entailment problems

We study two basic problems of probabilistic reasoning: the probabilistic logic and the probabilistic entailment problems. The first one can be defined as follows. Given a set of logical sentences and probabilities that these sentences are true, the aim is to determine whether these probabilities are consistent or not. Given a consistent set of logical sentences and probabilities, the probabilistic entailment problem consists in determining the range of the possible values of the probability associated with additional sentences while maintaining a consistent set of sentences and probabilities.This paper proposes a general approach based on an anytime deduction method that allows the follow-up of the reasoning when checking consistency for the probabilistic logic problem or when determining the probability intervals for the probabilistic entailment problem. Considering a series of subsets of sentences and probabilities, the approach proceeds by computing increasingly narrow probability intervals that either show a contradiction or that contain the tightest entailed probability interval. Computational experience have been conducted to compare the proposed anytime deduction method, called ad-psat with an exact one, psatcol, using column generation techniques, both with respect to the range of the probability intervals and the computing times.     

Toward a general theory of fuzzy variables

A general framework for a theory is presented that encompasses both statistical uncertainty, which falls within the province of probability theory, and nonstatistical uncertainty, which relates to the concept of a fuzzy set and possibility theory [L. A. Zadeh, J. Fuzzy Sets1 (1978), 3–28]. The concept of a fuzzy integral is used to define the expected value of a random variable. Properties of the fuzzy expectation are stated and a mean-value theorem for the fuzzy integral is proved. Comparisons between the fuzzy and the Lebesgue integral are presented. After a new concept of dependence is formulated, various convergence concepts are defined and their relationships are studied by using a Chebyshev-like inequality for the fuzzy integral. The possibility of using this theory in Bayesian estimation with fuzzy prior information is explored.     

A rough set approach to intuitionistic fuzzy soft set based decision making

The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. Since its appearance, there has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. The intuitionistic fuzzy soft set is a combination of an intuitionistic fuzzy set and a soft set. The rough set theory is a powerful tool for dealing with uncertainty, granuality and incompleteness of knowledge in information systems. Using rough set theory, this paper proposes a novel approach to intuitionistic fuzzy soft set based decision making problems. Firstly, by employing an intuitionistic fuzzy relation and a threshold value pair, we define a new rough set model and examine some fundamental properties of this rough set model. Then the concepts of approximate precision and rough degree are given and some basic properties are discussed. Furthermore, we investigate the relationship between intuitionistic fuzzy soft sets and intuitionistic fuzzy relations and present a rough set approach to intuitionistic fuzzy soft set based decision making. Finally, an illustrative example is employed to show the validity of this rough set approach in intuitionistic fuzzy soft set based decision making problems.     

Mixed-integer column generation algorithms and the probabilistic maximum satisfiability problem

The column generation approach to large-scale linear programming is extended to the mixed-integer case. Two general algorithms, a dual and a primal one, are presented. Both involve finding k-best solutions to combinatorial optimization subproblems. Algorithms for these subproblems must be tailored to each specific application. Their use is illustrated by applying them to a new combinatorial optimization problem with applications in Artificial Intelligence: Probabilistic Maximum Satisfiability. This problem is defined as follows: consider a set of logical sentences together with probabilities that they are true, assume this set of sentences is not satisfiable in the probabilistic sense, i.e., there is no probability distribution on the set of possible worlds (truth assignments to the sentences corresponding to at least one truth assignment to the logical variables they contain) such that for each sentence the sum of probabilities of the possible worlds in which it is true is equal to its probability of being true; determine a minimum set of sentences to be deleted in order to make the remaining set of sentences satisfiable. Computational experience with both algorithms is reported on.     

A Fuzzy Logic Approach for Determining Expected Values: A Project Management Application

This paper presents a methodology rooted in the general concepts of fuzzy logic theory with specific emphasis on belief functions and extension principles, and fuzzy probability distributions with fuzzy expectation based on fuzzy probability measures. This approach offers a useful alternative to the traditional approach in the estimation of probabilities in the absence of information about relative frequencies. An algorithm called BIPFET is developed and its application is demonstrated by utilizing data from a real-life research and development project.     

Probability of a fuzzy event: An axiomatic approach

Axioms are proposed that could justify the natural definition of the probability of a fuzzy event initially given by Zadeh. They are based (1) on the postulate that the sum of the conditional probability of a fuzzy event and of its complement given any fuzzy event adds to one or (2) on soft independence for orthogonal sets with independent constitutive elements. A general postulate is also required concerning the complement of a fuzzy set. The classical definition of the operator representing the complement can also be deduced.     

Imprecise set and fuzzy valued probability

Set valued probability and fuzzy valued probability theory is used for analyzing and modeling highly uncertain probability systems. In this paper the set valued probability and fuzzy valued probability are defined over the measurable space. They are derived from a set and fuzzy valued measure using restricted arithmetics. The range of set valued probability is the set of subsets of the unit interval and the range of fuzzy valued probability is the set of fuzzy sets of the unit interval. The expectation with respect to set valued and fuzzy valued probability is defined and some properties are discussed. Also, the fuzzy model is applied to binomial model for the price of a risky security.