关于概率论教学的一点思考

通过对概率论学科特点的分析和对高校数学教育根本目的理解,结合实际教学,把握高校概率论教学中的数学抽象思维培养的重要性,以适应高校数学的素质教育.     

概率论教学中思维品质的培养

从加强概率概念逻辑性教学、通过构造概率论中反例教学和构建相关问题的概率模型的教学等几个方面探讨了如何在概率论教学中培养学生的概率思维品质.     

谈案例教学法在概率论与数理统计教学中的应用

为了提高学生对概率论与数理统计知识的实际运用能力,针对概率论与数理统计学科特点,选取了几个教学案例,探讨了案例教学在这门学科教学中的优势和作用.本文着重从案例教学的思想来研究如何加强概率论与数理统计课程的应用性教学.     

《概率论与数理统计》教学中典型问题剖析

主要通过对《概率论与数理统计》教学中出现问题较多的几个概念的分析,阐述了如何通过适当的教学方法和教学手段,以达到较好的教学效果.     

概率论与数理统计研究型教学的案例分析

通过娃哈哈赠券收集的案例分析提出了概率论与数理统计习题课研究型教学的概念和方法,展示了概率论与数理统计在社会生活领域的一些生动应用.通过以案例驱动开展研究型教学实现了习题课课堂教学的趣味性,体现了知识的实用性,激发了学生们的学习兴趣和协作探究意识,进一步将自主学习和协作探究相结合提升了课堂的教学效果,培养了学生的数学思维习惯.     

《概率论》教学基本要求(讨论稿)

<正> 概率论是研究随机现象客观规律性的数学学科,是高等工业学校中一门重要的基础课。通过本课程的教学,使学生掌握概率论的基本概念,了解它的基本理论和方法。从而,使学生初步掌握处理随机现象的基本思想和方法,培养运用概率论的方法去分析和解决实际问题的能力。一、随机事件与概率     

概率论课程中蕴含的数学文化

数学文化是概率论课程的重要组成部分,因此在概率论课程教学中非常有必要展示所蕴含的数学文化,才能使概率论课程教学在培养学生的工程数学学习与应用技能的同时,又能形成良好的数学思维与素养.从概率论的发展历程、概率论的数学思想与方法,概率实际应用案例等几方面阐明概率论中蕴含的数学文化.概率论课程教学与数学文化的融合能够解读枯燥的概率知识,降低概率知识的抽象性,用数学文化自身的魅力吸引学生,培养学生的创新应用等综合素质.     

从对立事件谈逆向思维的训练

在《概率论》教学中,通过对学生进行逆向思维的训练,可有效地提高学生的思维能力.     

在《概率论与数理统计》教学实践中反思《高等数学》教学

通过对概率论与数理统计课程的教学实践进行深入挖掘,从内容和思想方法两个方面探索了《高等数学》与《概率论与数理统计》课程之间的关系.内容方面主要体现在离散型随机变量与无穷级数、连续型随机变量和积分、最大似然估计和最值理论等方面,而思想方法方面主要体现在利用微积分的重要思想建立概率统计中的相关概念和相关理论.通过对两门课程的关系进行梳理,一方面可以更好的进行概率统计课程的教学,同时也可以为高等数学教学提供有益的借鉴.     

浅析数学文化引导下的概率论与数理统计教学

数学文化是人类物质文明产物,也是精神文明产物,它来源于数学的形成过程,又影响数学的发展;它是数学的灵魂,将原本生硬的数学教学过程变得明晰、简洁、充满人文气息.本文通过对文化、数学文化及概率论与数理统计教学的诠释,从人类物质文明产物和精神文明产物两个方面系统阐述数学文化的特点;通过对工科概率论与数理统计教学特点的审视,阐述数学文化在概率与数理统计教学中的必要性,讨论如何在工科专业学开展数学文化引导下的概率与数理统计教学及其发展趋势.     

概率论中事件的分割思想及其应用

讨论概率论中事件的分割思想,给出一类概率论考研题的新解法,并探讨两个不相关(相关系数为零)的随机变量非独立的判别方法.     

关于二维连续型随机变量独立性的判断

随机变量的独立性是概率论中最基本的概念之一，通过对它的研究可使一些实际问题的概率模型的具体计算得到简化。因而，关于随机变量的独立性的研究构成了概率的重要课题。本文仅就二维连续型随机变量，给出了两种判断其分量独立性的理论和具体方法，并对其进行了比较。     

二次型在N重广义积分计算中的应用

积分上下限皆为无穷的Ｎ重广义积分，在工程技术特别是在概率论与数理统计中有着重要应用．但传统的积分方法对这类积分往往难以奏效，本文介绍一种利用二次型理论计算这类广义积分的方法．     

An interactive fuzzy satisficing method for multiobjective stochastic linear programming problems through an expectation model

For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.     

Some applied problems of stability of stochastic models

We consider several applied problems of probability theory. These problems are related to biophysics, mathematical economics, and automated control theory, and are considered in the framework of the theory of stability of stochastic models. Proceedings of the Seminar on Stability Problems for Stochastic Models, Moscow, Russia, 1996, Part I.     

Smooth and Semismooth Newton Methods for Constrained Approximation and Estimation

In the article, we show that the constrained L ₂ approximation problem, the positive polynomial interpolation, and the density estimation problems can all be reformulated as a system of smooth or semismooth equations by using Lagrange duality theory. The obtained equations contain integral functions of the same form. The differentiability or (strong) semismoothness of the integral functions and the Hölder continuity of the Jacobian of the integral function were investigated. Then a globalized Newton-type method for solving these problems was introduced. Global convergence and numerical tests for estimating probability density functions with wavelet basis were also given. The research in this article not only strengthened the theoretical results in literatures but also provided a possibility for solving the probability density function estimation problem by Newton-type method.     

Equivalence classes of regularly varying functions

A useful method to derive limit results for partial maxima and record values of independent, identically distributed random variables is to start from one specific probability distribution and to extend the result for this distribution to a class of distributions.This method involves an extended theory of regularly varying functions. In this paper, equivalence classes of regularly varying functions (in the extended sense) are studied, which is relevant to the problems mentioned above.     

Weak convergence in applied probability

Weak convergence of probability measures on function spaces has been active area of research in recent years. While the theory has a somewhat abstract base, it is extremely useful in a wide variety of problems and we believe has much to offer to applied probability. Our aim in this survey paper is to discuss those aspects of the theory which are relevant to work in applied probability. After an introduction to the foundations of weak convergence, we shall discuss partial sum, point, Markov and extremal processes. These processes form the building blocks for many of the important models of applied probability.     

Classroom note: Waiting for a sequence pattern in independent multinomial observations

The expected value (mean) is one of the most important measures of central tendency used both in statistics and in the mathematical theory of probability. Using the method of geometric transforms (probability generating functions), the expected number of observations is obtained until the sequence pattern 123…N123…N…123…N, which is an outcome in a sequence of independent multinomial observations. The results and the flow graph analysis obtained agree with and provide the generalization of similar problems considered earlier in this journal.     

SEARCH THEORY IN NATURAL RESOURCE MODELING

The role of search theory in the exploitation of natural resources is discussed in this paper. After a brief history and taxonomy of search problems, the mathematics of search is discussed. This includes underlying probability distributions, the differential equations of search, Bayesian use of search information and optimization problems in search theory. The theory is illustrated by applications in fisheries, pest control, animal foraging, and oil and mineral exploration.