统计代写|决策与风险作业代写decision and risk代考|An Integrated Bayesian BWM

如果你也在 怎样代写决策与风险decision and risk这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。




损失大小的概率分布,在某个规定的时间段,如一年。这就是我认为大多数人在谈论某物的 “风险 “时的真正含义。

statistics-lab™ 为您的留学生涯保驾护航 在代写决策与风险decision and risk方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写决策与风险decision and risk方面经验极为丰富,各种代写决策与风险decision and risk相关的作业也就用不着说。

我们提供的决策与风险decision and risk及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等楖率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
统计代写|决策与风险作业代写decision and risk代考|An Integrated Bayesian BWM

统计代写|决策与风险作业代写decision and risk代考|Classifiable TOPSIS Model

Risk assessment has been regarded as one of the scientific fields for about 40 years. Initially, how to conceptualize risk assessment was a question discussed by researchers at that time. After that, many concepts, principles, theories, methods,

and frameworks for assessing risk have been gradually developed. These studies still deeply influence the field of risk assessment (Aven 2016). Many novel risk assessment methods have brought many contributions and new trends in this field. Newer, more effective, more comprehensive, and more systematic assessment models are the common goal of researchers. A risk decision model is proposed by Hansson and Aven (2014), the model divides risk assessment into five stages, including evidence, knowledge base, broad risk evaluation, decision-makers review, and decision. The first three stages are fact-based, providing evidence through testing and collecting data or information about risk events. Related expert groups are based on these data or information to further research and analyze risks. In addition, the last three stages are value-based. Because risk events are complex and difficult to fully illustrate and explain through scientific tools, after extensive risk assessment, it is necessary to review and judge by decision-makers before final decisions can be made. The purpose of risk analysis is to eliminate and control potential risk events or hazardous factors, so as to reduce the occurrence of accidents and diminish the severity of accidents (Lo et al. 2019).

Research on risk analysis can be divided into three types, including qualitative analysis, semi-quantitative analysis, and quantitative analysis (Marhavilas et al. 2011 ; Mutlu and Altuntas 2019). Qualitative analysis is to explore whether the research subjects have special attributes or characteristics, and whether they are related or not, through observation and analysis experiments. The data recorded through expert interviews is a way of qualitative analysis. Common qualitative analysis techniques are checklist, what if analysis, safety audits, task analysis, sequentially timed events plotting (STEP), human factors analysis and classification system (HFACS), and hazard and operability study (HAZOP). Quantitative analysis is the quantitative relationship among the components contained in a research object, or the quantitative relationship among the characteristics that it possesses, and it can also analyze and compare the special relationships, characteristics, and properties of several objects quantitatively simultaneously. Therefore, the analysis results are mostly described and solved in terms of quantity. Quantitative analysis techniques include clinical risk and error analysis (CREA), proportional risk-assessment (PRAT), decision matrix risk-assessment (DMRA), societal risk, predictive epistemic approach (PEA), and quantitative risk-assessment (QRA). The semi-quantitative analysis is somewhere in between. In the risk assessment, the collected observation data and survey information use semi-quantitative analysis to perform risk assessment. Semi-qualitative quantitative analysis techniques include event tree analysis (ETA), failure mode and effects analysis (FMEA), fault tree analysis (FTA), risk-based maintenance (RBM), and human error analysis techniques (HEAT/HFEA). The above-mentioned risk assessment and analysis tools are popular methods used by academic researchers and risk analysts today (Marhavilas et al. 2011; Lo et al. 2019; Mutlu and Altuntas 2019; Chang et al. 2019; Gul et al. 2020; Yucesan and Gul 2020; Lo et al. 2020; Liou et al. 2020).

统计代写|决策与风险作业代写decision and risk代考|FMEA and MCDM

The FMEA program commences with a review of design details, illustrations of equipment block diagrams, and recognition of all possible failures, consecutively. Following recognition, all potential causes and effects should be classified to the related failure modes. After this, failure modes are prioritized based on their destructive effects and ranked by a risk rating (Lo and Liou 2018). More methods are being combined with FMEA to optimize the risk assessment model, with MCDM being the most outstanding (Huang et al. 2020). The relevant MCDM-based FMEA model in the past five years is presented in Table 2.1.

The above-mentioned literatures have made great contributions to the field of risk assessment, making semi-quantitative analysis more effective. After extensive FMEA literature review, this study found some research gaps. As mentioned earlier, FMEA is based on risk analyst judgement to construct a risk assessment matrix. However, in some cases, if risk analysts cannot make appropriate semantic judgments, they can only compare the risk levels from the existing failure modes. In addition, only three risk factors are considered in most RPN calculations, and only a few researches have discussed the management costs of failure modes.

统计代写|决策与风险作业代写decision and risk代考|BWM

The operation of BWM is easy to understand and to obtain highly consistent results, so it has been widely used in decision-making problems in various industries. BWM plays a very important role in this research. It is not only used to evaluate the weights of risk factors, but also used to evaluate the degrees of risks of failure modes. BWM was proposed by Rezaei (2015), and it is mainly used to overcome the limitations and shortcomings of AHP. Figure $2.1$ shows a schematic diagram of the conventional pairwise comparison method. The evaluation system has five evaluated items, and the number of pairwise comparisons is $10[n(n-1) / 2 \Rightarrow 5 *(4) / 2=10]$. The pairwise comparison concept proposed by BWM is shown in Fig. 2.2. Experts or decisionmakers or risk analysts select the most and least important evaluated items (best and worst evaluated items), and then make pairwise comparisons. Using the same example, BWM only needs 7 pairwise comparisons $[2 n-3 \Rightarrow 2 *(5)-3=7]$.

Tables $2.2,2.3$, and $2.4$ illustrate the questionnaire design pattern associated with the AHP and BWM. From the perspective of the experts or decision-makers or risk analysts answering the questionnaires, the BWM questionnaire is more logical and consistent. In Tables $2.2,2.3$, and $2.4$, the light gray shaded cells indicate the evaluation information input by the experts. In the AHP example, considering the goal (evaluation issue), a number from $1 / 9$ to 9 is assigned to show the preference of a specific evaluated item over the others (filled in light gray shaded cells). It is worth mentioning that BWM uses a number between 1 and 9 to show the preference of evaluated item $i$ over the evaluated item $j$.
The steps of BWM in FMEA can be summarized as follows (Rezaei 2015):
Step 1. Construct a set of risk factors.
The experts or decision-makers or risk analysts form an FMEA team, and they formulate evaluation risk factors $\left(C_{1}, C_{2}, \ldots, C_{j}, \ldots, C_{n}\right)$ for decision-making problems.

统计代写|决策与风险作业代写decision and risk代考|An Integrated Bayesian BWM


统计代写|决策与风险作业代写decision and risk代考|Classifiable TOPSIS Model

大约 40 年来,风险评估一直被视为科学领域之一。最初,如何将风险评估概念化是当时研究人员讨论的问题。之后,许多概念、原理、理论、方法,

风险评估框架逐步形成。这些研究仍然深刻影响风险评估领域(Aven 2016)。许多新的风险评估方法为该领域带来了许多贡献和新趋势。更新、更有效、更全面、更系统的评估模型是研究人员的共同目标。Hansson and Aven (2014)提出了一个风险决策模型,该模型将风险评估分为五个阶段,包括证据、知识库、广泛的风险评估、决策者审查和决策。前三个阶段是基于事实的,通过测试和收集有关风险事件的数据或信息来提供证据。相关专家组根据这些数据或信息进一步研究和分析风险。此外,最后三个阶段是基于价值的。由于风险事件复杂,难以用科学工具充分说明和解释,经过广泛的风险评估,需要决策者进行审查和判断,才能做出最终决策。风险分析的目的是消除和控制潜在的风险事件或危险因素,从而减少事故的发生,降低事故的严重程度(Lo et al. 2019)。

风险分析研究可分为定性分析、半定量分析和定量分析三种类型(Marhavilas et al. 2011 ; Mutlu and Altuntas 2019)。定性分析是通过观察和分析实验,探索研究对象是否具有特殊的属性或特征,以及它们是否相关。通过专家访谈记录的数据是一种定性分析的方式。常见的定性分析技术有清单、假设分析、安全审计、任务分析、时序事件绘图 (STEP)、人为因素分析和分类系统 (HFACS) 以及危害和可操作性研究 (HAZOP)。定量分析是研究对象所含成分之间的定量关系,或它所具有的特性之间的数量关系,也可以同时定量地分析和比较几个对象的特殊关系、特性和性质。因此,分析结果大多以数量来描述和解决。定量分析技术包括临床风险和错误分析(CREA)、比例风险评估(PRAT)、决策矩阵风险评估(DMRA)、社会风险、预测认知方法(PEA)和定量风险评估(QRA)。半定量分析介于两者之间。在风险评估中,收集到的观测数据和调查信息采用半定量分析进行风险评估。半定性定量分析技术包括事件树分析(ETA),故障模式和影响分析 (FMEA)、故障树分析 (FTA)、基于风险的维护 (RBM) 和人为错误分析技术 (HEAT/HFEA)。上述风险评估和分析工具是当今学术研究人员和风险分析师使用的流行方法(Marhavilas et al. 2011; Lo et al. 2019; Mutlu and Altuntas 2019; Chang et al. 2019; Gul et al. 2020; Yucesan 和 Gul 2020;Lo 等人 2020;Liou 等人 2020)。

统计代写|决策与风险作业代写decision and risk代考|FMEA and MCDM

FMEA 计划从连续审查设计细节、设备框图图解和识别所有可能的故障开始。识别后,应将所有潜在的原因和影响归类为相关的故障模式。在此之后,故障模式根据其破坏性影响进行优先级排序,并按风险等级排序(Lo and Liou 2018)。更多方法正在与 FMEA 相结合以优化风险评估模型,其中 MCDM 最为突出(Huang et al. 2020)。过去五年中基于 MCDM 的相关 FMEA 模型如表 2.1 所示。

上述文献对风险评估领域做出了巨大贡献,使半定量分析更加有效。经过广泛的 FMEA 文献回顾,本研究发现了一些研究空白。如前所述,FMEA 是基于风险分析师的判断来构建风险评估矩阵。但是,在某些情况下,如果风险分析师无法做出适当的语义判断,他们只能从现有的故障模式中比较风险级别。此外,大多数 RPN 计算仅考虑三个风险因素,只有少数研究讨论了故障模式的管理成本。

统计代写|决策与风险作业代写decision and risk代考|BWM

BWM的操作简单易懂,结果一致性高,因此被广泛应用于各行业的决策问题。BWM 在这项研究中起着非常重要的作用。它不仅用于评估风险因素的权重,还用于评估故障模式的风险程度。BWM 由 Rezaei (2015) 提出,主要用于克服层次分析法的局限性和不足。数字2.1显示了传统的成对比较方法的示意图。评价系统有五个评价项目,两两比较的次数为10[n(n−1)/2⇒5∗(4)/2=10]. BWM 提出的成对比较概念如图 2.2 所示。专家或决策者或风险分析师选择最重要和最不重要的评估项目(最佳和最差评估项目),然后进行成对比较。使用同样的例子,BWM 只需要 7 次成对比较[2n−3⇒2∗(5)−3=7].

表2.2,2.3, 和2.4说明与 AHP 和 BWM 相关的问卷设计模式。从回答问卷的专家或决策者或风险分析师的角度来看,BWM问卷更加符合逻辑和一致性。在表格中2.2,2.3, 和2.4,浅灰色阴影单元格表示专家输入的评价信息。在 AHP 示例中,考虑到目标(评估问题),来自1/9分配到 9 以显示特定评估项目相对于其他项目的偏好(填充为浅灰色阴影单元格)。值得一提的是,BWM 使用一个介于 1 和 9 之间的数字来表示被评估项目的偏好一世在评估的项目上j.
FMEA 中 BWM 的步骤可总结如下(Rezaei 2015):
步骤 1. 构建一组风险因素。
专家或决策者或风险分析师组成 FMEA 团队,他们制定评估风险因素(C1,C2,…,Cj,…,Cn)对于决策问题。

统计代写|决策与风险作业代写decision and risk代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考


在概率论概念中,随机过程随机变量的集合。 若一随机系统的样本点是随机函数,则称此函数为样本函数,这一随机系统全部样本函数的集合是一个随机过程。 实际应用中,样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化,随机运动如布朗运动、随机徘徊等等。


贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布,和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型,后验分布可以解析或近似,例如,马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要,包括点估计,如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外,所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。





随着AI的大潮到来,Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比,Machine Learning在其他领域也有着广泛的应用,因此这门学科成为不仅折磨CS专业同学的“小恶魔”,也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多,跨学科范围广,所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题,StudyGate专业导师团队都能为你轻松解决。


基础数据: $N$ 个样本, $P$ 个变量数的单样本,组成的横列的数据表
变量定性: 分类和顺序;变量定量:数值
数学公式的角度分为: 因变量与自变量


随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。



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