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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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|贝叶斯网络代写Bayesian network代考|Mapping of FT-ESD Model to BN

To conduct a case-specific failure probability analysis, a BN of ED failure was developed through mapping the FT-ESD model shown in Fig. 5. The left part of the FT-ESD model is FT; mapping from the FT into the BN includes a graphical and numerical translation [38]. In the graphical step, the structure of $\mathrm{BN}$ is developed from the FT such that primary events, intermediate events, and the top events of the FT are represented as root nodes, intermediate nodes, and the leaf nodes in the equivalent $\mathrm{BN}$, respectively. The nodes of $\mathrm{BN}$ are connected in the same way as the corresponding events in the FT. In the numerical step, occurrence probabilities of the primary events are assigned to the corresponding root nodes as prior probability. For each intermediate node as well as the leaf node, a CPT is assigned. CPTs illustrate how intermediate nodes are related to precedent intermediate or root nodes.

The right part of the FT-ESD model is ESD; mapping from the ESD into BN model is just like that of a FT being mapped into BN model, which also includes a graphical and numerical translation. Similarly, in the graphical step, the structure of $\mathrm{BN}$ is developed from the ESD such that initial event, comment events, and the termination events of the ESD are represented as top node, intermediate nodes, and the consequence nodes in the equivalent $\mathrm{BN}$, respectively. In the numerical step,

for each intermediate node as well as the consequence node, a CPT is assigned. CPTs are defined according to the process parameters $P r$ in the ESD model. In the ESD mapping process in this study, the possible events, the conditions controlling the development of an event sequence into different branches, logic relation for evolution of the ED failure accident, and the occurrence probability, which represent the process parameters, were all determined by expert judgments.

Because of the uncertainty and complexity, it is difficult to determine the prior probabilities for BEs and conditional probability tables for different factors. In the present work, the expert judgments were used for this purpose. Five related experts from the oil company as listed in Table 3 were invited to judge the fuzzy number and CPTs based on their experiences.

## 统计代写|贝叶斯网络代写Bayesian network代考|Risk Updating

In addition to offering a flexible structure and a robust reasoning engine, the main application of BN is in risk updating. In risk updating, the probability of an accident scenario is updated. This updating is performed in terms of posterior probability of event $x i$ given a new evidence. This also helps to identify the critical basic (the most probable) cause of events leading to the evidence $[3,40]$. The most common type of evidence used in probability updating is the knowledge about the top event or consequences.

In the present study, the posterior probabilities of BEs given a ED failure, i.e., $\mathrm{P}$ (xilconsequence $=\mathrm{ED}$ failure), are shown in the last column of Table 2. By com-paring the posterior probabilities with the prior probabilities of BEs, the critical BEs could be identified. The critical BEs, which are the BEs with a high posterior probability and a high increasing probability, would provide meaningful information for ED operations and preventive actions to avoid ED failure [5]. In Table 2, it is observed that $\mathrm{X} 15, \mathrm{X} 17, \mathrm{X} 20, \mathrm{X} 22, \mathrm{X} 26, \mathrm{X} 27, \mathrm{X} 28$, and $\mathrm{X} 35$ have the highest increasing probability and significant posterior probability values (Fig. 6). Therefore, the critical events in ED failure are X15 (inadequate overpull), X17 (large inclination angle of wellhead), X20 (formation of natural gas hydrate at LMRP connector), X22 (unreasonable disconnect moment), X26 (internal solitary waves), X27 (typhoon), X28 (local rapidly developing storm), and X35 (high unlock pressure in connector events during drilling riser design and ED operations.

GeNIe software can also implement strength of influence, through which the probable development paths are found (U.O. Pittsburgh, 2014). The most probable accident evolution paths for ED failure were as follows: (i) $\mathrm{X} 15$ (inadequate overpull) and X17 (large inclination angles of conductor), and X20 (formation of natural gas hydrate at the connector) $\rightarrow$ adverse operation factors $\rightarrow$ ED failure; (ii) $\mathrm{X} 22$ (unreasonable disconnect moment during the heave cycle of the vessel) and X27 (typhoon) $\rightarrow$ adverse time factors $\rightarrow$ ED failure; (iii) X35 (high unlock pressure in connector induced by leakage of corrosion) $\rightarrow$ malfunction of control factors $\rightarrow$ ED failure. The most probable consequence evolution paths of the ED failure were as follows: (i) ED failure $\rightarrow$ riser break just above the LMRP $\rightarrow$ safe suspension by the vessel (after anti-recoil operation); (ii) ED failure $\rightarrow$ riser break just below the rotary table $\rightarrow$ riser break above LMRP $\rightarrow$ sinking to the seabed.

# 贝叶斯网络代考

## 统计代写|贝叶斯网络代写Bayesian network代考| FT-ESD Model到BN的映射

FT-ESD模型右侧为ESD;从ESD映射到BN模型就像FT映射到BN模型一样，还包括图形和数字的转换。类似地，在图形化步骤中，$\mathrm{BN}$的结构是从ESD开发的，这样ESD的初始事件、注释事件和终止事件分别表示为等价的$\mathrm{BN}$中的顶层节点、中间节点和结果节点。在数值步长中，

## 统计代写|贝叶斯网络代写Bayesian network代考|风险-更新

. name BN除了提供一个灵活的结构和一个健壮的推理引擎之外，它的主要应用是风险更新。在风险更新中，更新事故场景的概率。这个更新是根据事件$x i$的后验概率进行的，给出了一个新的证据。这也有助于识别导致证据的事件的关键基本(最可能的)原因$[3,40]$。在概率更新中使用的最常见的证据类型是关于顶部事件或结果的知识 在本研究中，ED失败时BEs的后验概率，即， $\mathrm{P}$ (xilconsequence $=\mathrm{ED}$ failure), are shown in the last column of Table 2. By com-paring the posterior probabilities with the prior probabilities of BEs, the critical BEs could be identified. The critical BEs, which are the BEs with a high posterior probability and a high increasing probability, would provide meaningful information for ED operations and preventive actions to avoid ED failure [5]. In Table 2, it is observed that $\mathrm{X} 15, \mathrm{X} 17, \mathrm{X} 20, \mathrm{X} 22, \mathrm{X} 26, \mathrm{X} 27, \mathrm{X} 28$，以及 $\mathrm{X} 35$ 因此，ED失效的关键事件为X15(超拉不足)、X17(井口倾角大)、X20 (LMRP接头处形成天然气水合物)、X22(断开力矩不合理)、X26(内部孤立波)、X27(台风)、X28(局部快速发展的风暴)和X35(钻井隔水管设计和ED作业期间接头处发生的高解锁压力事件 GeNIe软件还可以实现影响力的强度，通过它可以找到可能的开发路径(u.o Pittsburgh, 2014)。ED失效最可能的事故演化路径为:(i) $\mathrm{X} 15$(超拉不足)和X17(导体倾角大)、X20(连接器处天然气水合物形成)$\rightarrow$不良操作因素$\rightarrow$ ED失效;(ii) $\mathrm{X} 22$(船舶升沉周期中不合理的断开力矩)和X27(台风)$\rightarrow$不利时间因素$\rightarrow$ ED故障;(iii) X35(泄漏腐蚀导致连接器解锁压力高)$\rightarrow$控制因素故障$\rightarrow$ ED故障。ED失效最可能的后果演变路径如下:(i) ED失效$\rightarrow$ LMRP上方立管破裂$\rightarrow$被船安全悬挂(反后坐操作后);(ii) ED故障$\rightarrow$转盘下方立管断裂$\rightarrow$ LMRP上方立管断裂$\rightarrow$沉入海底。

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## MATLAB代写

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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