基于蒙特卡罗模拟的项目风险量化模型

一、蒙特卡罗模拟蒙特卡罗法,(Monte-Carlo Simulation)又称统计实验法或随机模拟法。该法是一种通过对随机变量的统计实验,随机模拟,求解数学、物理、工程技术问题近似解的数学方法。其特点是用数学方法在计算机上模拟实际概率过程,然后加以统计处理。     

基于蒙特卡罗法的边坡可靠度分析

传统边坡稳定性分析常以安全系数作为评价指标,然而,安全系数并未考虑各影响因素的变异性及不确定性,其大小无法体现边坡的安全程度。可靠度分析方法将概率统计和可靠度理论引入边坡工程稳定性分析中,考虑了各影响因素不确定性,将设计参数作为随机变量,得到可靠度或失效概率作为边坡安全程度的评价指标,不是简单给出安全或失稳的结论。以某一边坡工程为例,基于蒙特卡罗法对其进行可靠度分析,说明该方法的合理性及实用性。     

蒙特卡罗仿真的原理及应用

蒙特卡罗法又称随机抽样技巧法或统计试验法,在目前结构可靠度计算中,它被认为是一种相对精确法,具有在计算机上实现蒙特卡罗计算时程序结构清晰简单,便于编制和调试的特点。     

浅谈概率统计课堂中的趣味教学法

为了推动概率统计课程的教学改革,提高学生学习概率统计课程的积极性,结合作者多年的教学经验,提出了趣味教学法,首先要培养学生的兴趣,在此基础上循序渐进引导学生掌握这门课的学习方法,寓教于乐,从而达到教学目的。     

女子院校“概率统计”课程在线教学模式探究

"概率统计"是女子本科院校人才培养的一门重要的专业基础课程。文章首先分析了当前女子院校"概率统计"在线教学的背景、存在的问题和意义。其次,以超星学习通为教学平台,从教学内容模块设计、教学过程、在线教学考核评价这三个方面探讨"概率统计"课程在线教学模式。最后提出"概率统计"课程在线教学模式的改革措施。     

财经院校概率与数理统计课程教学法的一些思考

文章根据财经院校概率统计课程的教学经验,提出了在教学过程中要尽量激发并提高学生的学习兴趣,注重采用启发式教学、类比法教学、对比法教学。同时,采用多媒体进行教学,增加案例教学,提高教学效果和学生的动手能力。     

蒙特卡洛法在投资项目风险分析中的应用

本文以经济净现值为评价指标来度量项目的投资风险,确定各影响因素的概率分布,建立了基于三角分布的风险评价的随机模型,采用蒙特卡罗方法进行模拟,利用MATLAB编程实现,得到投资项目的净现值频数分布的直方图和累计频率曲线图,并对模拟结果进行统计和分析,可得到净现值的平均预测值以及风险率,为评价投资项目的风险提供了理论依据。     

概率统计教学研究

在中学数学进行大幅度的改革后,随着概率统计向高中数学的下方,工程数学如何适应中学数学改革与社会进步的要求,改革概率统计教学、课程体系,是工程数学教育必须直面的一个问题。文章探讨了高中数学课改的影响,分析了目前概率统计教育与中学数学课改不相适应的现状,分析了目前招收的学生普遍认为概率统计比较难学原因,提出了概率统计教育改革的内容与目标。     

蒙特卡罗模拟在项目财务评价中的应用

把蒙特卡罗方法运用于项目财务评价,可以通过构造数学模型与计算机仿真得到相对精确的评价值概率分布,并对该评价结果的有效性进行一定程度的讨论。     

《PLC编程训练》课程教学实践中的体会与思考

梯形图程序编辑是PLC编程训练学习的核心内容。本文介绍了PLC编程训练课的教学设计,并介绍了经验编程法在本课程学习中的应用,另结合自身的教学体会,提出了各类问题的解决方法。     

随机数在随机事件与概率教学中的应用探索

本文对在随机事件与概率中的常见问题提出了一种新的借助于计算机实现的解决思路,即通过机器自动产生随机数后构建相应的数学模型,再通过编程计算得到最后结果。该方法既为概率论的教学带来了创新性思维,也为当前的教师提出了新的挑战。     

Advanced Multilevel Monte Carlo Methods

This article reviews the application of some advanced Monte Carlo techniques in the context of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations, which can be biassed in some sense, for instance, by using the discretization of an associated probability law. The MLMC approach works with a hierarchy of biassed approximations, which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider some Markov chain Monte Carlo and sequential Monte Carlo methods, which have been introduced in the literature, and we describe different strategies that facilitate the application of MLMC within these methods.     

野战油料装备维修任务分配研究

分析了战时油料装备维修任务分配问题,利用蒙特卡洛方法对维修任务分配进行了计算机仿真模拟,并通过实例验证了该方法对解决战时油料装备维修任务分配问题的优越性。     

VB程序设计教学方法改革与探讨

VB是一种面向对象的程序设计语言,它功能强大、应用广泛,是各高校普遍开设的一门课程,本文根据VB程序设计课程特点,分析了课堂教学和实验教学现状,提出了改革方法,并建议以网络平台辅助教学,同时改革考核方式,以达到提高教学质量的目的。     

ON BOOTSTRAPPING A CLASS OF DIFFERENTIABLE STATISTICAL FUNCNONALS WITH APPLICATIONS TO L– AND M– ESTIMATES

EFRON'S (1979) "bootstrap" method is justified for a class of differentiable statistical functionals which includes many L – and M –statistics. For illustration, a Monte Carlo study for the trimmed means is also included.     

蒙特卡洛模拟法下已知服务水平的定量订货法研究

库存是企业经营不可缺少的部分,运用科学的库存管理方法可以为企业节约成本。利用Visual FoxPro V6.0软件,采用蒙特卡洛模拟法,通过计算机随机数取日销售量和订货前置期的长度,进而计算出前置期的销售量,这样重复N次,由此计算出各自的概率分布及累计概率,在给定的库存满足率的情况下就可以求出订货点,实现定量订货法两个指标的控制。     

Empirical likelihood block bootstrapping

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.     

Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis

Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.