浅谈蒙特卡罗法在概率统计教学中的应用

在概率统计教学中利用蒙特卡罗法设计适当的模拟实验,结合多媒体在课堂教学中实时演示,能很好地加深学生对概率统计概念、思想的理解,进而培养学生的统计思维能力.     

Introducing computational thinking through hands-on projects using R with applications to calculus,probability and data analysis

The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data analysis. We present nine computer experiments and suggest a few more, with applications to calculus, probability and data analysis, which engage computational thinking through simulations, visualizations and data analysis. We are using the free (open-source) statistical programming language R. Our goal is to give a taste of what R offers rather than to present a comprehensive tutorial on the R language. In our experience, these kinds of interactive computer activities can be easily integrated into a smart classroom. Furthermore, these activities do tend to keep students motivated and actively engaged in the process of learning, problem solving and developing a better intuition for understanding complex mathematical concepts.     

Conceptual variation and coordination in probability reasoning

This study investigates students’ conceptual variation and coordination among theoretical and experimental interpretations of probability. In the analysis we follow how Swedish students (12-13 years old) interact with a dice game, specifically designed to offer the students opportunities to elaborate on the logic of sample space, physical/geometrical considerations and experimental evidence when trying to develop their understanding of compound random phenomena.The analytical construct of contextualization was used as a means to provide structure to the qualitative analysis performed. Within the frame of the students’ problem encounters during the game and how they contextualized the solutions of the problems in personal contexts for interpretations, the analysis finds four main forms of appearance, or of limitations in appearance, of conceptual variation and coordination among theoretical and experimental interpretations of probability.     

Testing for equality of means of a Hilbert space valued random variable

We propose a test for equality of means of a random variable valued into a real separable Hilbert space. The test statistic is based on projections of empirical means onto spaces spanned by principal directions obtained from principal component analysis of the random variable. The asymptotic distribution of this test statistic is derived under the null hypothesis and the consistency of the obtained test is proved. An application to the case of functional variables is indicated. To cite this article: J.G. Aghoukeng Jiofack, G.M. Nkiet, C. R. Acad. Sci. Paris, Ser. I 347 (2009).     

Invariance principles for sums of Banach space valued random elements and empirical processes

Summary Almost sure and probability invariance principles are established for sums of independent not necessarily measurable random elements with values in a not necessarily separable Banach space. It is then shown that empirical processes readily fit into this general framework. Thus we bypass the problems of measurability and topology characteristic for the previous theory of weak convergence of empirical processes.Both authors were partially supported by NSF grants. This work was done while the second author was visiting the M.I.T. Mathematics Department     

Stochastic optimization on Bayesian nets

In this paper we are concerned with stochastic optimization problems in the case when the joint probability distribution, associated with random parameters, can be described by means of a Bayesian net. In such a case we suggest that the structured nature of the probability distribution can be exploited for designing efficient gradient estimation algorithm. Such gradient estimates can be used within the general framework of stochastic gradient (quasi-gradient) solution procedures in order to solve complex non-linear stochastic optimization problems. We describe a gradient estimation algorithm and present a case study related to the reliability of semiconductor manufacturing together with numerical experiments.     

上海股票市场收益的随机波浪模型

对金融资产收益分布状况的主要研究方法是先提出分布模型,然后进行实验验证;因缺乏必要的机理分析和研究手段单一,使其理论研究和应用研究都受到一定的制约.为克服这些不足,将金融资产收益联系起来看,根据其涨跌周期性构建出随机波浪模型,并利用模型导出随机波浪波高和周期的分布公式.通过实证分析,证明随机波浪模型具有一定的适用性;所用的时频分析方法以及所得结论有益于对金融资产收益分布状况进行更深入的理论和应用研究,也有益于指导市场参与者进行短期和长期交易.     

Investigating the transformation of a secondary teacher’s knowledge of trigonometric functions

Shulman (1987) defined pedagogical content knowledge as the knowledge required to transform subject-matter knowledge into curricular material and pedagogical representations. This paper presents the results of an exploratory case study that examined a secondary teacher’s knowledge of sine and cosine values in both clinical and professional settings to discern the characteristics of mathematical schemes that facilitate their transformation into learning artifacts and experiences for students. My analysis revealed that the teacher’s knowledge of sine and cosine values consisted of uncoordinated quantitative and arithmetic schemes and that he was cognizant only of the behavioral proficiencies these schemes enable, not the mental actions and conceptual operations they entail. Based on these findings, I hypothesize that the extent to which a teacher is consciously aware of the mental activity that comprises their mathematical conceptions influences their capacity to transform their mathematical knowledge into curricular material and pedagogical representations to effectively support students’ conceptual learning.     

A functional central limit theorem for empirical processes under a strong mixing condition

This paper introduces a functional central limit theorem for empirical processes endowed with real values from a strictly stationary random field that satisfies an interlaced mixing condition. We proceed by using a common technique from Billingsley (Convergence of probability measures, Wiley, New York, 1999), by first obtaining the limit theorem for the case where the random variables of the strictly stationary ???-mixing random field are uniformly distributed on the interval [0, 1]. We then generalize the result to the case where the absolutely continuous marginal distribution function is not longer uniform. In this case we show that the empirical process endowed with values from the ???-mixing stationary random field, due to the strong mixing condition, doesn??t converge in distribution to a Brownian bridge, but to a continuous Gaussian process with mean zero and the covariance given by the limit of the covariance of the empirical process. The argument for the general case holds similarly by the application of a standard variant of a result of Billingsley (1999) for the space D(???, ??).     

An approximation scheme for quasi-stationary distributions of killed diffusions

In this paper we study the asymptotic behavior of the normalized weighted empirical occupation measures of a diffusion process on a compact manifold which is killed at a smooth rate and then regenerated at a random location, distributed according to the weighted empirical occupation measure. We show that the weighted occupation measures almost surely comprise an asymptotic pseudo-trajectory for a certain deterministic measure-valued semiflow, after suitably rescaling the time, and that with probability one they converge to the quasi-stationary distribution of the killed diffusion. These results provide theoretical justification for a scalable quasi-stationary Monte Carlo method for sampling from Bayesian posterior distributions.     

Loss data analysis: Analysis of the sample dependence in density reconstruction by maxentropic methods

The problem of determining probability densities of positive random variables from empirical data is important in many fields, in particular in insurance and risk analysis. The method of maximum entropy has proven to be a powerful tool to determine probability densities from a few values of its Laplace transform. This is so even when the amount of data to compute numerically the Laplace transform is small. But in this case, the variability of the reconstruction due to the sample variability in the available data can lead to quite different results. It is the purpose of this note to quantify as much as possible the variability of the densities reconstructed by means of two maxentropic methods: the standard maximum entropy method and its extension to incorporate data with errors.The issues that we consider are of special interest for the advanced measurement approach in operational risk, which is based on loss data analysis to determine regulatory capital, as well as to determine the loss distribution of risks that occur with low frequency.     

Resource allocation on condition of random resource demand of network activities

A general network model is considered. Each activity may require several storable and productive resources. Fixed amounts of storable resources are delivered at fixed times. The capacities of the productive resources are assumed to be constant. A general resource allocation problem is derived in order to optimize the cost for the execution of the network activities, In the main, the case is considered where the resource demands of the activities are random variables. For this case, the means of resource demands are estimated and a method is presented to obtain a vector of start times and durations of the activities so that a cost function is minimized and the probability that, at any time, there appears no lack of resources is maximized.     

Sharp probability bounds for the binomial moment problem with symmetry

Sharp bounds in terms of the first few binomial moments are found for the probability of a union of events, when the random variable denoting the number of events that occur follows symmetric distribution. Connection between the bounds of this paper and the bounds from a special case of the binomial moment problem of Prekopa (1995) is shown. As a special case, bounds for the probability when the underlying probability distribution is unimodal-symmetric are also found.     

Connecting experimental probability and theoretical probability

In this reflective paper, I explore the thinking of a group of pre-service teachers as they reason about experimental probability and theoretical probability. I am particularly interested in investigating whether pre-service teachers could construct a bidirectional link between the experimental probability and theoretical probability, similar to the tentative model I introduce elsewhere (2008) for coordinating the two perspectives on distribution. Overall, this research study contributes to understanding how pre-service students can build connections to help teachers conceptualize and support students to embrace elements that act as connections between the two approaches to probability.     

Rates of convergence for partial mass problems

We consider a class of partial mass problems in which a fraction of the mass of a probability measure is allowed to be changed (trimmed) to maximize fit to a given pattern. This includes the problem of optimal partial transportation of mass, where a part of the mass need not be transported, and also trimming procedures which are often used in statistical data analysis to discard outliers in a sample (the data with lowest agreement to a certain pattern). This results in a modified, trimmed version of the original probability which is closer to the pattern. We focus on the case of the empirical measure and analyze to what extent its optimally trimmed version is closer to the true random generator in terms of rates of convergence. We deal with probabilities on ${\mathbb{R}^k}$ and measure agreement through probability metrics. Our choices include transportation cost metrics, associated to optimal partial transportation, and the Kolmogorov distance. We show that partial transportation (as opposed to classical, complete transportation) results in a sharp decrease of costs only in low dimension. In contrast, for the Kolmogorov metric this decrease is seen in any dimension.     

Stochastic modelling of dynamic properties of nonlinear water waves

This paper describes the stochastic modelling of water waves and presents predictions of their structure. The work is based on the analysis of more than 10⁵ data points, obtained in the form of wave records during experiments in a channel¹. Both turbulent wind-driven waves and artificially generated waves superimposed on the former, were studied. The wave records were considered as random time series of events; and amplitudes, surface elevations and other characteristics were considered as stochastic processes, characterisable by their moments which were calculated from the wave records by means of a computer program. Despite the two-dimensional character of the flow, the suggested distributions are not inconsistent with oceanic data, provided that the latter are treated as two-dimensional.     

Interval distribution function of a bounded chaotic sequence as a basis of nonaxiomatic probability theory

We introduce the notion of interval distribution function of random events on the set of elementary events and the notion of interval function of the frequencies of these events. In the limiting case, the interval function turns into the ordinary distribution function and the interval function of frequencies (under certain conditions) turns into the density of distribution of random events. The case of discrete sets of elementary events is also covered. This enables one to introduce the notion of the probability of occurrence of random events as a result of the limit transition. Translated From Ukrains'kyi Matematychnyi Zhurnal, Vol. 60, No. 8, Pp. 1128–1137, August, 2008.     

Traditional instruction in advanced mathematics courses: a case study of one professor’s lectures and proofs in an introductory real analysis course

It is widely accepted by mathematics educators and mathematicians that most proof-oriented university mathematics courses are taught in a “definition-theorem-proof” format. However, there are relatively few empirical studies on what takes place during this instruction, why this instruction is used, and how it affects students’ learning. In this paper, I investigate these issues by examining a case study of one professor using this type of instruction in an introductory real analysis course. I first describe the professor’s actions in the classroom and argue that these actions are the result of the professor’s beliefs about mathematics, students, and education, as well as his knowledge of the material being covered. I then illustrate how the professor’s teaching style influenced the way that his students attempted to learn the material. Finally, I discuss the implications that the reported data have on mathematics education research.     

Competing trade mechanisms and monotone mechanism choice

We investigate the choice between posted prices and auctions of competing sellers with private valuations. Assuming that buyers face higher hassle costs in auctions, we show the existence of monotone pure strategy equilibria where sellers offer posted prices rather than auctions if and only if they have a sufficiently high reservation value. Posted prices sell with lower probability but yield a larger revenue in case of trade. Using an empirical strategy to compare revenues of posted prices and auctions that takes selling probabilities explicitly into account, we find our theoretical predictions supported by data from eBay auctions on ticket sales for the EURO 2008 European Football Championship.     

不等式证明中的概率思想方法

不等式的证明往往比较复杂,有时直观含义也比较抽象,代数的方法难以发挥作用。如果能够建立适当的概率模型,赋以一些随机事件或随机变量的具体含义,再利用概率的理论加以证明,则常常能使证明过程得到简化。同时还可以为抽象的数学问题提供具体的概率背景,沟通各数学分支之间的联系。文中通过几个不等式的证明阐明了常用的概率思想方法。