## 统计代写|贝叶斯网络代写Bayesian network代考|ENGG2100

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|Drift-Off

Drift-off is an event normally caused by loss of power, malfunction in the power system, engine breakdown, or mechanical and human errors. When the DP system can no longer hold the position, the increasing offset of the drilling vessel due to wind, wave, and current will cause large horizontal force and bending moment to the subsea wellhead by drilling riser system, and the ED must be activated to avoid possible accident. If the ED operations cannot be completed successfully in $60 \mathrm{~s}$ at most, it may damage the wellhead or break the riser joints. Once the integrity of the well is damaged, the blowout accident will occur inevitably. According to the existing literature, it has been stated that the occurrence probability of drift-off event is $2 \times 10^{-3}$ per year [17].

Establishing alert offsets for the ED of the vessel-connected riser system through drift-off analysis is used to determine the point of disconnect. Generally, the alert offsets settings are as follows: green region-drilling normally; yellow region-stop drilling and make the preparation for ED while the riser is in the “connected nondrilling mode”; red region-the ED is initiated automatically (it can also be initiated manually in advance) and must be completed before reaching the blue region; blue region-the suspended riser column is in survival mode [18].

A drive-off is much the same as a drift-off, but it comes from a malfunction in the DP system causing the rig to drive off from its location. This is a very critical event due to the higher velocity of the vessel, and it provides a short available time to activate the ED before the horizontal offset gets too large. The occurrence probability of drive-off event is $1.6 \times 10^{-5}$ per DP hour $[19,20]$.

## 统计代写|贝叶斯网络代写Bayesian network代考|Failure Probability Analysis Technology

ESD is a graphical method for visualizing the sequence of related events. As an effective risk assessment method, ESD has been used in many different fields [23]. The first ESD framework was proposed for risk modeling by NASA in the Cassini space program, and since then it has been employed widely by different researchers [24]. Wu [25] built an ESD model for the driving pump of a spaceship cooling circuit with an initiating event “power failure,” and analyzed the related accidents. Zhou et al. [23] applied ESD to evaluate emergency response actions during fireinduced domino effects. To assess the ED failure probability in the present study, ESD was defined based on the work of Swaminathan and Smidts [26].
$$\mathrm{ESD}=\left(E, C_{\mathrm{d}}, G, \operatorname{Pr}\right)$$
where $E$ refers to the events which implies any changes from one state to another. Any observable physical phenomenon the analyst chooses to represent in an ESD would be considered as an event. These events could be time-distributed events, demand-based events, non-quantifiable events, or end states. In the present work, events were divided into three categories: (1) “initial event”, being the beginning event of an ESD, and starting the potential event sequence; (2) “comment event”, describing the development of an event sequence, and (3) “termination event”, indicating the termination of the ESD. The symbols used to represent such events and brief definitions are given in Table $1 .$
$C_d$ indicatess conditions which reepresesent the ruless controlling the devèlopment of an event sequence into different branches. The event sequence will develop in different directions depending on whether the conditions are satisfied or not.
$G$ represents the logic gates, indicating the logical relationships among events. The basic gates are the AND gate and the OR gate, which can be further divided into four types according to event relationships, i.e., output AND gate, input AND gate, output OR gate, and input OR gate. These gates can be used to represent various situations like concurrent processes, synchronization processes, and multiple mutually exclusive outcomes. Especially, for the output $\mathrm{OR}$ gates, since the outcomes are mutually exclusive, only one of the many possible outcomes will occur. Figure 1 shows an example of an output OR gate. After the occurrence of Event 1, there are three possible scenarios. If $P 2, P 3$, and $P 4$ are the probabilities of occurrence of the three events, respectively, then their summation is equal to 1.Pr is a set of process parameters, which reflect the states of the system. For example, the abovementioned occurrence probabilities of the three events are the process parameters, which will influence the evolution of the accident and eventually the probabilities of the termination events (end states).

## 统计代写|贝叶斯网络代写贝叶斯网络代考|故障概率分析技术

ESD是一种可视化相关事件序列的图形化方法。作为一种有效的风险评估方法，ESD已经应用于许多不同的领域。第一个ESD框架是由NASA在卡西尼太空计划中为风险建模提出的，从那时起它就被不同的研究人员广泛采用。Wu[25]建立了飞船冷却回路驱动泵的ESD模型，并对其启动事件“断电”进行了分析。Zhou等人[23]应用ESD评估火灾诱发骨牌效应期间的应急响应行动。为了评估本研究中ED失败的概率，基于Swaminathan和Smidts[26]的工作定义了ESD。
$$\mathrm{ESD}=\left(E, C_{\mathrm{d}}, G, \operatorname{Pr}\right)$$
，其中$E$表示从一个状态到另一个状态的任何变化。分析师选择在ESD中表示的任何可观察到的物理现象都将被视为一个事件。这些事件可以是时间分布的事件、基于需求的事件、不可量化的事件或最终状态。在本文中，将事件分为三类:(1)“初始事件”，作为ESD的开始事件，并开始潜在事件序列;(2)“注释事件”，描述事件序列的发展，以及(3)”终止事件”，表示ESD的终止。表$1 .$中给出了用于表示此类事件的符号和简要定义，
$C_d$表示控制事件序列devèlopment到不同分支的规则条件。根据条件是否满足，事件序列将向不同的方向发展。
$G$表示逻辑门，表示事件之间的逻辑关系。基本门是与门和或门，根据事件关系可进一步分为四种类型，即输出与门、输入与门、输出或门和输入或门。这些门可以用来表示各种情况，比如并发过程、同步过程和多个互斥的结果。特别是对于输出$\mathrm{OR}$门，由于结果是互斥的，所以只会出现许多可能结果中的一个。图1显示了输出或门的示例。事件1发生后，有三种可能的场景。如果$P 2, P 3$和$P 4$分别是这三个事件发生的概率，那么它们的和等于1。Pr是反映系统状态的一组工艺参数。例如，上述三个事件的发生概率是过程参数，它将影响事故的演变，最终影响终止事件(结束状态)的概率。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|IMC012

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|Results and Discussions

Reliability and availability evaluation The reliabilities and availabilities within 100 weeks are evaluated by the forward inference, shown in Fig. 7. The coverage factors for the redundant system are assigned to $0.95$. As indicated in Fig. 7a, the reliabilities decrease with time. The reliability of CDD system is higher than HDD system, whereas the TMR system is between them. Moreover, the reliabilities of CDD, TMR, and HDD systems at 100th week are $0.81,0.785$, and $0.771$, respectively. As indicated in Fig. 7b, the occurrence probabilities of degraded state for the CDD, TMR, and HDD systems increase to $0.065,0.064$, and $0.063$ at 100th week, respectively.

As shown in Fig. 7c, the availability of CDD, TMR, and HDD systems is $0.999923$, $0.999909$, and $0.999902$, respectively. The availabilities of the three onboard systems approach steady values in 10 weeks. The high availabilities indicate that the onboard systems can recover rapidly when the primary system suffers a failure. Obviously, the availabilities accord with design specification that it should be greater than $0.9999 .$

## 统计代写|贝叶斯网络代写Bayesian network代考|Deepwater Drilling Riser System

Deepwater drilling conductor is the first layer of casing installed during the well construction in deepwater drilling, which is generally jetted into the formation without well cementing. After jetting the conductor with low-pressure wellhead (LPW), completing the installation of the casing surface tubular with high pressure wellhead (HPW), and cementing, drilling operation is followed by deployment of riser system and LMRP/BOP by making up the riser joints.

The main components of the riser column include BOP/LMRP stack, lower flex joint (LFJ), slick and buoyancy riser joints, telescopic joint (TJ), and upper flex joint (UFJ). The top end of the riser column is connected to the drilling vessel through the tension system. The TJ consists of inner and outer barrels where the relative motion (stroke) of these barrels can compensate for the length variations of riser column with the motion of the drilling vessel. The LFJ and UFJ can improve the mechanical performance for both ends of the riser column to avoid excessive bending moment and hence damage to the risers [16].

The subsea BOP/LMRP stack includes LMRP and BOP, which is usually equipped with two hydraulic connectors, namely the LMRP connector and wellhead connector.

The LMRP connector is located in the middle of two annular preventers, which is used to connect the LMRP to the BOP, and the wellhead connector is used to connect $\mathrm{BOP}$ and $\mathrm{HPW}[7,8]$. If $\mathrm{ED}$ is activated automatically or manually under extreme conditions, the LMRP will disconnect from BOP at the LMRP connector, and the riser column will be lifted up and suspended by the tensioners eventually after the disconnect is completed. If there is drill pipe in the drilling riser, the blind shear rams in BOP will cut through the pipe and seal the well before disconnect.

.

## 统计代写|贝叶斯网络代写贝叶斯网络代考|深水钻井隔水管系统

LMRP连接器位于两个环形防喷器的中间，用于连接LMRP和BOP，井口连接器用于连接$\mathrm{BOP}$和$\mathrm{HPW}[7,8]$。如果$\mathrm{ED}$在极端条件下自动或手动激活，则LMRP将在LMRP连接器处与防喷器断开，在断开完成后，隔水管柱将被张紧器抬起并悬挂。如果钻井隔水管中有钻杆，防喷器中的盲切闸板将切断钻杆，并在断开前密封井

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|PHYS4016

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|DBN Structure Modeling

The structure modeling presents the mapping rules from DFT into DBN. Here, we briefly model the OR gate, AND gate, 2003 voting gate, and spare gate as they will be later used in the case study. For those static logic gates (the OR gate, AND gate, and 2003 voting gate), mapping rules are described in the previous study [24]. As indicated in Fig. 3b, the relationship between $\mathrm{C} 1, \mathrm{C} 2$, and $\mathrm{A}$ is linked by intra-slice arcs. Each node involves two states denoted by Working (W) or Failed (F). The significant feature, coverage factor $c$, in a redundant system is taken into account to model the inaccuracy of a recovery mechanism. The coverage factor $c$ is presented as $c=$ probability {system recoverslfault occurs $}$ [30]. Then, DBN extends the BN by incorporating temporal dependencies at different time slices. For instance, the node $\mathrm{C} 1(t)$ is extended to $\mathrm{C} 1(t+\Delta t)$ with a temporal arc. Similarly, the DBNs of OR gate and 2003 voting gate are shown in Fig. 3a and c, respectively.

Dynamic logic gates are designed to express the time sequence and failure behaviors of the systems. The priority AND (PAND) gate, the functional dependency (FDEP) gate, and the spare gate are commonly used in DFT modeling. The mapping rules of spare gate are described based on the previous study [25, 26]. Generally, a spare gate consists of two types of elements: the primary modules and one or multiple redundant modules. For example, in Fig. 3d, the DBN structure is similar to that in Fig. 3a and $\mathrm{b}$, but, the former one demonstrates that component $\mathrm{S}$ at $t+\Delta t$ time slice is dependent on both $\mathrm{P}$ at $t$ time slice and $\mathrm{S}$ at $t$ time slice. Assume that the primary $\mathrm{P}$ is active at the $t$ time slice with liailure rate $\lambda$, and the lailure rate of one spare S is $\lambda$ in active state or $\alpha \lambda$ at inactive state, where $\alpha$ is the dormancy factor. Hot and cold spares can be modeled by setting $\alpha$ equal to 1 and 0 , respectively. Whenever the $P$ fails, a replacement is initiated and the $S$ will be powered up to keep the system functional.

## 统计代写|贝叶斯网络代写Bayesian network代考|Determination of DBN Parameters

DBN parameters are based on the prior probabilities of root nodes and the CPT of intermediate nodes and leaf nodes. The node $\mathrm{Cl}$ in Fig. 3b is demonstrated as an example. Assuming $\mathrm{Cl}$ follows the exponential distribution with failure rate $\lambda$, it can be obtained:
$$P{\mathrm{Cl}(t+\Delta t)=F \mid \mathrm{Cl}(t)=F}=1-e^{-\lambda t}$$
Considering a repair action, the availability of $\mathrm{Cl}$ can also be obtained. If the repair rate of $\mathrm{Cl}$ is $\mu$, it can be obtained:
$$P{\mathrm{Cl}(t+\Delta t)=W \mid \mathrm{C1}(t)=F}=1-e^{-\mu t}$$
The CPT for $\mathrm{C} 1$ at $t+\Delta t$ time slice given $\mathrm{Cl}$ at $t$ time slice is provided in Tables 1 and $2 .$

For spare gate shown in Fig. 3d, the CPT of node $S$ without and with repair are given in Tables 3 and 4 , where $\alpha$ is the dormancy factor.

Through the forward inference, the reliability and availability of different redundancy strategy can be obtained. Meanwhile, the posterior probabilities of each node are generated by the backward inference after an evidence is introduced. A sensitivity analysis is carried out with the assumption that the prior probabilities of five function modules are subject to the uncertainty of $10 \%$. Moreover, the effects of coverage factor on reliability and availability will be calculated.

The validation of the proposed approach is a significant procedure to prove that it is reasonable for the reliability and availability evaluation of the actual system. In this paper, the validations are accomplished in two ways: A partial validation of the model usability should satisfy three axioms proposed by Jones et al. [31]. The availabilities obtained from the proposed approach are validated by analyzing the field data of one railway bureau in China.

## 统计代写|多元统计分析代写多元统计分析代考|波士顿住房

\begin{aligned} X_{14}=& \beta_0+\beta_4 X_4+\beta_5 X_5+\beta_6 X_6+\beta_8 X_8+\beta_9 X_9+\beta_{10} X_{10}+\beta_{11} X_{11} \ &+\beta_{12} X_{12}+\beta_{13} X_{13} \end{aligned}

## 统计代写|多元统计分析代写多元统计分析代考|分类反应

.

$$L=\frac{n !}{\prod_{k=1}^K y_{k} !} \prod_{k=1}^K\left(\frac{m_k}{n}\right)^{y_k} .$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|ENGG2100

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|Reliability and Availability

As shown in Fig. 6, the reliability and availability of the PV systems with centralized, string, and multistring configurations in the presence of intermittent faults are calculated and plotted. Intermittent fault coefficient $x$, permanent fault coefficient $y$, and intermittent repair coefficient $z$ are set to 40,20 , and $50 \%$, respectively.

The reliabilities of the three PV systems decrease with the increase of time (Fig. 6 a, b). In particular, the reliability of the PV system with centralized configuration rapidly decreases, compared with the other systems. The PV system with string configuration has high reliability in the first five years, whereas the PV system with multistring configuration has a high reliability after five years. The main reason behind this case is the fact that the redundant DC/AC inverters first lead to a high system reliability of the PV system with string configuration first. A low failure rate of the $\mathrm{DC} / \mathrm{AC}$ inverter subsequently leads to a high system reliability of the PV system with multistring configuration Therefore, in terms of reliability, the multistring configuration is the best choice in designing PV systems, whereas the centralized configuration is the worst choice.

The comparison of the reliability in Fig. $6 \mathrm{a}$, b indicates that the intermittent faults just slightly affect the reliability values in the first ten years. The three PV systems with intermittent faults have a slightly higher reliability than those without intermittent faults. The average reliability increments of ten years for centralized, string, and multistring configurations are $0.50,0.78$, and $0.40 \%$, respectively. This is because that the intermittent faults can transform to no faults, which is autorecovery. This finding is attributed to the fact that the intermittent faults can be transformed into “no faults,” which is autorecovery.

## 统计代写|贝叶斯网络代写Bayesian network代考|Mutual Information Investigation

Mutual information measures the information shared by two variables and determines the degree of uncertainty of reduction of one variable by knowing one of the other variables [34]. This information can be used to identify the degree of importance of each PV component to the entire PV system. In this study, the degree of importance in three moments, i.e., first, fifth, and tenth years, is investigated, as shown in Fig. 8 . The degree of importance of the components of the three PV systems is the same, which is in the order of DC/AC inverter, DC/DC converter, DC combiner, and PV module arranged from largest to smallest. This degree increases with the increase of time. The $\mathrm{DC} / \mathrm{AC}$ inverter is determined to affect the reliability of the $\mathrm{PV}$ system significantly with multistring configuration, whereas the other components only exert a few contributions. Therefore, the DC/AC inverter should be given considerable attention to improve the reliability and availability of PV systems and to prevent their possible failures. The failure rates of the components of a PV system with a specified configuration should be reduced, but their repair rates should be increased to improve the reliability and availability of such system. Hence, the DC/AC inverter with low failure rates should be used in design and manufacturing stages of PV systems. Moreover, the repair rate of this component should be increased in the usage stage.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|IMC012

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|DBN Structure Modeling

A BN is generally constructed through two major procedures, namely the construction of structure models and the definition of parameter models [46]. In the first step, a set of relevant variables and their possible values should be decided. A network structure can then be set up by connecting these variables into a directed acyclic graph. In the second step, the conditional probability table for each network variable should be defined.

The DBN structure models for the PV systems with centralized, string, and multistring configurations in the presence of intermittent faults are constructed (Fig. 3) according to the PV system configurations given in Fig. 1. Figure 3a demonstrates that the failure of any PV component in a PV system with centralized configuration will cause the failure of the entire PV system. This case signifies that the PV components, including four PV modules #1, #2, #3, and #4 (i.e., PV1, PV2, PV3, and PV4), two DC combiners (Comb1 and Comb2), a DC/DC converter (Conv), and a DC/AC inverter (Inve), are considered a series. Therefore, the network structure is built with two layers using the Netica software tool. The first layer consists of eight nodes representing the status of eight PV components. Each node has three states, i.e., the fault not existing state (NF), intermittent faulty state (IF), and permanent faulty state (PF). The second layer includes one node that depicts the status of PV system. This node has two states, i.e., work and fail, which indicate whether the whole PV system is working or not.

DBNs are an extension of the general BNs that allow the explicit modeling of changes over time. In this process, each time step is called a time slice. Figure 3a indicates that the DBNs of the PV system with centralized configuration consist of two time slices, that is, from $t=0$ to $t=\Delta t$. The nodes PV1, PV2, PV3, PV4, Comb1, Comb2, Conv, and Inve at $t=0$ are extended to PV5, PV6, PV 7, PV8, Comb3, Comb4, Conv1, and Inve1 at $t=\Delta t$, respectively. The number of time slice and the value of $\Delta t$ are determined by the purpose of research and the time the Netica runs. A great number of time slices correspond to a smaller value of $\Delta t$, and, hence, a longer time at which Netica runs. The DBN structure models for the PV systems with string and multistring configurations are similar to that for the PV system with centralized configuration and are produced based on the series and parallel relationship of the PV components, as shown in Fig. 3b, c. The DBN structure model of the complex PV system is given in Fig. 4. The series and parallel relationship among the PV components establishes the conditional probability tables of nodes, which are described in the subsequent section.

## 统计代写|贝叶斯网络代写Bayesian network代考|Intermittent Fault Modeling

Intermittent faults can hardly be modeled using a directed DBN structural modeling directed. Therefore, this study proposes a method that fuses the Markov model into a DBN model. The developed method has four basic assumptions specified as follows [47-50]:
(1) The PV systems begin with a perfect operation, in which all PV components are functioning correctly.
(2) The transition rates of the PV components, including the failure and repair rates are different, but constant. The lifetimes of these components are exponentially distributed because they are mainly electronic products.
(3) The states of all components are statistically independent.
(4) The PV systems are considered “as good as new” after repairs.
The idea of intermittent and permanent faults can be incorporated in terms of the three-state Markov models as shown in Fig. $5[25,26]$. The model stipulates that the NF state can be converted into a PF and IF states with a failure rate $\lambda_{1}$ and $\lambda_{2}$, respectively. An intermittent fault can lead the components into PF and NF states. Therefore, the IF state can become a PF state with a failure rate of $\lambda_{3}$ and to an NF state with a repair rate of $\mu_{1}$ (autorecovery), as shown in Fig. 5a. If a failed component is repaired once permanent fault occurs, then a repair arc should be added to the state transition diagram. In this case, the PF state can become an NF state with a repair rate of $\mu_{2}$ (manual repair), as shown in Fig. $5 \mathrm{~b}$. When the repair action is not considered, the reliability of the PV system can be calculated. When the repair action is considered, the availability of the PV system can be calculated using the proposed DBN model.

## 统计代写|贝叶斯网络代写Bayesian network代考|DBN Structure Modeling

BN通常通过两个主要程序构建，即结构模型的构建和参数模型的定义[46]。第一步，应确定一组相关变量及其可能值。然后可以通过将这些变量连接到有向无环图中来建立网络结构。第二步，定义每个网络变量的条件概率表。

DBN 是通用 BN 的扩展，允许对随时间的变化进行显式建模。在这个过程中，每个时间步称为一个时间片。图 3a 表明集中配置光伏系统的 DBN 由两个时间片组成，即从吨=0至吨=D吨. 节点 PV1、PV2、PV3、PV4、Comb1、Comb2、Conv 和 Inve 在吨=0扩展到 PV5、PV6、PV 7、PV8、Comb3、Comb4、Conv1 和 Inve1吨=D吨， 分别。时间片的数量和值D吨由研究目的和 Netica 运行时间决定。大量的时间片对应于较小的值D吨，因此，Netica 的运行时间更长。组串和多串配置光伏系统的DBN结构模型与集中配置光伏系统相似，是根据光伏组件的串联和并联关系生成的，如图3b、c所示。复杂光伏系统的DBN结构模型如图4所示。光伏组件之间的串联和并联关系建立了节点的条件概率表，将在下一节中描述。

## 统计代写|贝叶斯网络代写Bayesian network代考|Intermittent Fault Modeling

1）光伏系统从完美运行开始，其中所有光伏组件都正常运行。
(2) 光伏组件的转换率，包括故障率和修复率是不同的，但是是恒定的。这些组件的寿命呈指数分布，因为它们主要是电子产品。
(3) 所有组件的状态在统计上是独立的。
(4) 光伏系统在维修后被认为“和新的一样好”。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|PHYS4016

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|Research Directions

In view of the literature review of BN-based reliability evaluation methodologies, a few upcoming research directions in this field that are of interest to reliability researchers and practitioners are presented in this section.
A. BN Modeling Methods Considering Cascading Failures
Failure dependency remarkably affects the reliability of systems, particularly hardware and structures. Common cause failure and cascading failure are two typical examples of failure dependency. BN modeling methods considering common cause failure have been extensively researched $[25,101]$. A cascading failure is a failure in an interconnected system, in which the failure of a part can trigger the failure of successive parts. Few studies on the $\mathrm{BN}$-based reliability evaluation methodology have considered cascading failures [102]. For hardware and structure reliability evaluation, constructing the structure and parameter models of BNs for reliability evaluation by considering the cascading failure of components, especially when temporal and dynamic features are involved, is a challenging problem.
B. $D B N-B a s e d$ Reliability Prediction for Software and Humans
Software and humans are not subject to degradation and aging when they are modeled for reliability evaluation. Software behavior changes with time because maintenance activities occur or the environment changes over time. Human errors are more complex than software errors because human reliability is influenced by intrinsic factors (e.g., skill) and external factors (e.g., weather). Reliability can be predicted well if the dynamic changes in the environmental factors related to software and human reliability can be modeled using DBNs.

## 统计代写|贝叶斯网络代写Bayesian network代考|Description of Grid-Connected PV Systems

A grid-connected PV system consists of PV modules and balance-of-system components. The $\mathrm{PV}$ modules can be arranged in different configurations that directly affect the structure and topology of the balance-of-system electronic components [35-37]. Different configurations of PV modules have been proposed during in the past, such as centralized, string, multistring, and modular configurations [38-40]. The balance-of-system components of PV systems include string protection, DC combiner, DC/DC converter, DC/AC inverter, DC disconnect, AC disconnect, grid protection, and others $[6,19,41-44]$.

In this study, three PV system configurations, i.e., centralized, string, and multistring configurations, are analyzed to compare their respective system reliabilities in the context of intermittent faults of electronic components. For simplicity, only a few electronic devices are considered, including PV module, DC. combiner, DC/DC. converter and DC/AC inverter [45]. Other electronic devices, such as controller, DC disconnect, $\mathrm{AC}$ disconnect, grid protection, are excluded from the study, as shown in Fig. $1 .$

For example, in consideration of the PV system with a centralized configuration illustrated in Fig. 1a, the PV array composed of two strings of two modules each connects a series-parallel configuration. Subsequently, the DC voltage level is combined together in a DC combiner, converted from $\mathrm{DC}$ to $\mathrm{DC}$ in a DC/DC converter and from $\mathrm{DC}$ to $\mathrm{AC}$ in a $\mathrm{DC} / \mathrm{AC}$ inverter, and is finally fed into the electricity grid system. $\mathrm{A}$ centralized configuration is mainly used in PV plants, which have a nominal power higher than $10 \mathrm{~kW}$, a high power conversion efficiency, and low cost. However, the maximuin power point tracking (MPPT) éfficiency of this central structure sharrply decreases in a partial shading condition because it can hardly individually draw the maximum power from each module, thereby decreasing total efficiency [38].

## 统计代写|贝叶斯网络代写Bayesian network代考|Research Directions

A. 考虑级联故障的 BN 建模方法

B.D乙ñ−乙一个s和d软件和人类的可靠性预测 在

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|ENGG2100

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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| Conditional Independence Tests

Conditional independence tests focus on the presence of individual arcs. Since each arc encodes a probabilistic dependence, conditional independence tests can be used to assess whether that probabilistic dependence is supported by the data. If the null hypothesis (of conditional independence) is rejected, the arc can be considered for inclusion in the DAG. For instance, consider adding an arc from Education to Travel $(\mathrm{E} \rightarrow \mathrm{T})$ to the DAG shown in Figure 1.1. The null hypothesis is that Travel is probabilistically independent $\left(\Perp_{P}\right)$ from Education conditional on its parents, i.e.,
$$H_{0}: \mathrm{T} \Perp_{P} \mathrm{E} \mid{0, \mathrm{R}},$$
and the alternative hypothesis is that
$$H_{1}: T \not H_{P} E \mid{0, \mathrm{R}} .$$
We can test this null hypothesis by adapting either the log-likelihood ratio $\mathrm{G}^{2}$ or Pearson’s $\mathrm{X}^{2}$ to test for conditional independence instead of marginal independence. For $\mathrm{G}^{2}$, the test statistic assumes the form
$$\mathrm{G}^{2}(\mathrm{~T}, \mathrm{E} \mid 0, \mathrm{R})=\sum_{t \in \mathrm{T}} \sum_{e \in \mathrm{E}} \sum_{k \in 0 \times \mathbb{R}} n_{t e k} \log \frac{n_{t e k} n_{++k}}{n_{t+k} n_{+e k}},$$
where we denote the categories of Travel with $t \in \mathrm{T}$, the categories of Education with $e \in \mathrm{E}$, and the configurations of Occupation and Residence with $k \in 0 \times \mathrm{R}$. Hence, $n_{t e k}$ is the number of observations for the combination of a category $t$ of Travel, a category $e$ of Education and a category $k$ of $0 \times R$. The use of a “+” subscript denotes the sum over an index, as in the classic book from Agresti (2013), and is used to indicate the marginal counts for the remaining variables. So, for example, $n_{t+k}$ is the number of observations for $t$ and $k$ obtained by summing over all the categories of Education. For Pearson’s $\mathrm{X}^{2}$, using the same notation we have that
$$\mathrm{X}^{2}(\mathrm{~T}, \mathrm{E} \mid 0, \mathrm{R})=\sum_{t \in \mathrm{T}} \sum_{e \in \mathrm{E}} \sum_{k \in 0 \times \mathbb{R}} \frac{\left(n_{t e k}-m_{t e k}\right)^{2}}{m_{t e k}}, \quad \text { where } \quad m_{t e k}=\frac{n_{t+k} n_{+e k}}{n_{++k}} .$$

## 统计代写|贝叶斯网络代写Bayesian network代考|Using the DAG Structure

Using the DAG we saved in dag, we can investigate whether a variable is associated with another, essentially asking a conditional independence query. Both direct and indirect associations between two variables can be read from the DAG by checking whether they are connected in some way. If the variables depend directly on each other, there will be a single arc connecting the nodes corresponding to those two variables. If the dependence is indirect, there will be two or more arcs passing through the nodes that mediate the association. In general, two sets $\mathbf{X}$ and $\mathbf{Y}$ of variables are independent given a third set $\mathbf{Z}$ of variables if there is no set of arcs connecting them that is not blocked by the conditioning variables. Conditioning on $\mathbf{Z}$ is equivalent to fixing the values of its elements, so that they are known quantities. In other words, the $\mathbf{X}$ and $\mathbf{Y}$ are separated by $\mathbf{Z}$, which we denote with $\mathbf{X} \Perp_{G} \mathbf{Y} \mid \mathbf{Z}$. Given that $\mathrm{BNs}$ are based on DAGs, we speak of $d$-separation (directed separation): a formal treatment of its definition and properties is provided in Section 6.1. For the moment, we will just say that graphical separation $\left(\Perp_{G}\right)$ implies probabilistic independence $\left(\Perp_{P}\right)$ in a $\mathrm{BN}$ : if all the paths between $\mathbf{X}$ and $\mathbf{Y}$ are blocked, $\mathbf{X}$ and $\mathbf{Y}$ are (conditionally) independent. The converse is not necessarily true: not every conditional independence relationship is reflected in the graph.
We can investigate whether two nodes in a bn object are d-separated using the dsep function. dsep takes three arguments, $x, y$ and $z$, corresponding to $\mathbf{X}, \mathbf{Y}$ and $\mathbf{Z}$; the first two must be the names of two nodes being tested for d-separation, while the latter is an optional d-separating set. So, for example, we can see from dag that both $S$ and 0 are associated with $R$.

## 统计代写|贝叶斯网络代写Bayesian network代考| Conditional Independence Tests

$$\mathrm{G}^{2}(\mathrm{~T}, \mathrm{E} \mid 0, \mathrm{R})=\sum_{t \in \mathrm{T}} \sum_{e \in \mathrm{E}} \sum_{k \in 0 \times \mathbb{R}} n_{t e k} \log \frac{n_{t e k} n_{++k}}{n_{t+k} n_{+e k}}$$

$$\mathrm{X}^{2}(\mathrm{~T}, \mathrm{E} \mid 0, \mathrm{R})=\sum_{t \in \mathrm{T}} \sum_{e \in \mathrm{E}} \sum_{k \in 0 \times \mathbb{R}} \frac{\left(n_{t e k}-m_{t e k}\right)^{2}}{m_{t e k}}, \quad \text { where } \quad m_{t e k}=\frac{n_{t+k} n_{+e k}}{n_{++k}} .$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|IMC012

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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| Probabilistic Representation

In the previous section we represented the interactions between Age, Sex, Education, Occupation, Residence and Travel using a DAG. To complete the BN modelling the survey, we will now specify a joint probability distribution over these variables. All of them are discrete and defined on a set of nonordered states (called levels in $\mathrm{R}$ ).

Therefore, the natural choice for the joint probability distribution is a multinomial distribution, assigning a probability to each combination of states of the variables in the survey. In the context of $\mathrm{BNs}$, this joint distribution is called the global distribution.

However, using the global distribution directly is difficult: even for small problems, such as that we are considering, the number of parameters involved is very high. In the case of this survey, the parameter set includes the 143 probabilities corresponding to the combinations of the levels of all the variables. Fortunately, we can use the information encoded in the DAG to break down the global distribution into a set of smaller local distributions, one for each variable. Recall that arcs represent direct dependencies: if there is an arc from one variable to another, the latter depends on the former. In other words, variables that are not linked by an arc are conditionally independent. As a result, we can factorise the global distribution as follows:
$$\operatorname{Pr}(A, S, E, 0, R, T)=\operatorname{Pr}(A) \operatorname{Pr}(S) \operatorname{Pr}(E \mid A, S) \operatorname{Pr}(0 \mid E) \operatorname{Pr}(R \mid E) \operatorname{Pr}(T \mid O, R)$$
Equation (1.1) provides a formal definition of how the dependencies encoded in the DAG map into the probability space via conditional independence relationships. The absence of cycles in the DAG ensures that the factorisation is well defined. Each variable depends only on its parents; its distribution is univariate and has a (comparatively) small number of parameters. The set of all the local distributions has, overall, fewer parameters than the global distribution. The latter represents a more general model than the former, because it does not make any assumption on the dependencies between the variables. In other words, the factorisation in Equation (1.1) defines a nested model or a submodel of the global distribution.

## 统计代写|贝叶斯网络代写Bayesian network代考|Learning the DAG Structure: Tests and Scores

In the previous sections we have assumed that the DAG underlying the BN is known. In other words, we rely on prior knowledge on the phenomenon we are modelling to decide which arcs are present in the graph and which are not. However, this is not always possible or desired; the structure of the DAG itself may be the object of our investigation. It is common in genetics and systems biology, for instance, to reconstruct the molecular pathways and networks underlying complex diseases and metabolic processes. An outstanding example of this kind of study can be found in Sachs et al. (2005) and will be explored in Chapter 8. In the context of social sciences, the structure of the DAG may identify which nodes are directly related to the target of the analysis and may therefore be used to improve the process of policy making. For instance, the

DAG of the survey we are using as an example suggests that train fares should be adjusted (to maximise profit) on the basis of Occupation and Residence alone.

Learning the DAG of a $\mathrm{BN}$ is a complex task, for two reasons. First, the space of the possible DAGs is very big; the number of DAGs increases superexponentially as the number of nodes grows. As a result, only a small fraction of its elements can be investigated in a reasonable time. Furthermore, this space is very different from real spaces (e.g., $\mathbb{R}, \mathbb{R}^{2}, \mathbb{R}^{3}$, etc.) in that it is not continuous and has a finite number of elements. Therefore, ad-hoc algorithms are required to explore it. We will investigate the algorithms proposed for this task and their theoretical foundations in Section 6.5. For the moment, we will limit our attention to the two classes of statistical criteria used by those algorithms to evaluate DAGs: conditional independence tests and network schrest.

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|PHYS4016

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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| Train-Use Survey

Consider a simple, hypothetical survey whose aim is to investigate the usage patterns of different means of transport, with a focus on cars and trains. Such surveys are used to assess customer satisfaction across different social groups, to evaluate public policies and to improve urban planning. Some real-world examples can be found, for instance, in Kenett et al. (2012).

In our current example we will examine, for each individual, the following six discrete variables (labels used in computations and figures are reported in parenthesis):

• Age (A): the age, recorded as young (young) for individuals below 30 years old, adult (adult) for individuals between 30 and 60 years old, and old (old) for people older than 60 .
• Sex (S): the biological sex, recorded as male (M) or female (F).
• Education (E): the highest level of education or training successfully completed, recorded as up to high school (high) or university degree (uni).
• Occupation (0): whether the individual is an employee (emp) or a selfemployed (self) worker.
• Residence (R): the size of the city the individual lives in, recorded as either small (small) or big (big).
• Travel (T): the means of transport favoured by the individual, recorded either as car (car), train (train) or other (other).
• In the scope of this survey, each variable falls into one of three groups. Age and Sex are demographic indicators. In other words, they are intrinsic characteristics of the individual; they may result in different patterns of behaviour but are not influenced by the individual himself. On the other hand, the opposite is true for Education, Occupation and Residence. These variables are socioeconomic indicators and describe the individual’s position in society. Therefore, they provide a rough description of the individual’s expected lifestyle; for example, they may characterise his spending habits and his work schedule. The last variable, Travel, is the target of the survey, the quantity of interest whose behaviour is under investigation.

## 统计代写|贝叶斯网络代写Bayesian network代考|Graphical Representation

The nature of the variables recorded in the survey, and more in general of the three categories they belong to, suggests how they may be related with each other. Some of these relationships will be direct, while others will be mediated by one or more variables (indirect).

Both kinds of relationships can be represented effectively and intuitively by means of a directed graph, which is one of the two fundamental entities characterising a BN. Each node in the graph corresponds to one of the variables in the survey. In fact, they are usually referred to interchangeably in the literature. Therefore, the graph produced from this example will contain six nodes, labelled after the variabless (A, S, E, $0, R$ and $T$ ). Direct dependence relationships are represented as arcs between pairs of variables (e.g., $A \rightarrow E$ means that $E$ depends on A). The node at the tail of the arc is called the parent, while that at the head (where the arrow is) is called the child. Indirect dependence relationships are not explicitly represented. However, they can be read from the graph as sequences of arcs leading from one variable to the other through one or more mediating variables (e.g., the combination of $A \rightarrow E$ and $E \rightarrow R$ means that $R$ depends on $A$ through $E$ ). Such sequences of arcs are said to form a path leading from one variable to the other; these two variables must be distinct. Paths of the form $\mathrm{A} \rightarrow \ldots \rightarrow \mathrm{A}$, which are known as cycles, are not allowed in the graph. For this reason, the graphs used in BNs are called directed acyclic graphs (DAGs).

Note, however, that some caution must be exercised in interpreting both direct and indirect dependencies. The presence of arrows or arcs seems to imply, at an intuitive level, that for each arc one variable should be interpreted as a cause and the other as an effect (e.g., $A \rightarrow E$ means that A causes $E$ ). This interpretation, which is called causal, is difficult to justify in most situations: for this reason, in general we speak about dependence relationships instead of causal effects. The assumptions required for causal BN modelling will be discussed in Section 6.7.

## 统计代写|贝叶斯网络代写Bayesian network代考| Train-Use Survey

• 性别（S）：生理性别，记为男性（M）或女性（F）。
• 教育（E）：成功完成的最高教育或培训水平，记录为高中（high）或大学学位（uni）。
• 职业（0）：个人是雇员（emp）还是个体经营者（self）。
• 居住地（R）：个人居住城市的大小，记为小（小）或大（大）。
• 旅行（T）：个人喜欢的交通工具，记为汽车（car）、火车（train）或其他（other）。
• 在本次调查的范围内，每个变量都属于三组之一。年龄和性别是人口统计指标。换句话说，它们是个体的内在特征；它们可能导致不同的行为模式，但不受个人本人的影响。另一方面，教育、职业和居住则相反。这些变量是社会经济指标，描述了个人在社会中的地位。因此，它们提供了个人预期生活方式的粗略描述；例如，它们可以描述他的消费习惯和工作日程。最后一个变量 Travel 是调查的目标，即正在调查其行为的兴趣数量。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 统计代写|贝叶斯网络代写Bayesian network代考|RE604

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯网络Bayesian network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。我们的专家在代写贝叶斯网络Bayesian network代写方面经验极为丰富，各种代写贝叶斯网络Bayesian network相关的作业也就用不着说。

• 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|Principle of semBnet

This section thoroughly explains the working principle of sembnet with respect to the following two major aspects, considering the spatial time series prediction scenario described in Sect. 5.3:

• Parameter learning
• Inference generation
semBnet extends standard Bayesian network analysis by incorporating domain knowledge, represented in terms of a semantic hierarchy [5]. In case of spatio-temporal prediction, semantic hierarchy is developed on the various concepts from the spatial domain and serves as the knowledge base to incorporate domain semantics in standard Bayesian analysis.

Typically, the semBnet consists of a qualitative component, comprising of a causal dependency graph (CDG), and a quantitative component, comprising of conditional probability distribution information for each of the nodes in the CDG.

Formally, the qualitative component of semBnet can be defined as a graph $G\left(V_{O}, V_{S}, E\right)$ which is directed as well as acyclic, where $V_{O}$ represents the set of nodes indicating random variables with no available semantics, $V_{S}$ represents the set of nodes indicating random variables with available semantics, and $E$ represents the set of edges between any two nodes in $\left(V_{O} \cup V_{S}\right)$. An edge from $V_{i} \in\left(V_{O} \cup V_{S}\right)$ to $V_{j} \in\left(V_{O} \cup V_{S}\right)$ indicates that variable $V_{i}$ influences variable $V_{j}$.
On the other side, the quantitative component of semBnet, i.e., the conditional probability distribution of any node $V_{x}$ in semBnet is represented as $P^{\uparrow}\left(V_{x} \mid\right.$ Parents $\left.\left(V_{x}\right)\right)$ if either $V_{x} \in V_{S}$ and/or $\left(\right.$ Parents $\left.\left(V_{x}\right) \cap V_{S}\right) \neq \emptyset$, where Parents $\left(V_{x}\right)$ denotes the set of parents or nodes influencing the target node $V_{x}$. Otherwise, the conditional probability is represented as that of the standard BN, i.e. $P\left(V_{x} \mid\right.$ Parents $\left.\left(V_{x}\right)\right)$.

## 统计代写|贝叶斯网络代写Bayesian network代考|Parameter Learning

This section illustrates the principle of semBnet learning in terms of marginal and conditional probability estimation.

For any node $V_{x} \in V_{O}$, the marginal probability $P\left(V_{x}\right)$ is estimated as that of a standard Bayesian network. However, if the node $V_{x}$ has available semantics (i.e. $V_{x} \in V_{S}$ ), the marginal probability is estimated as follows:
$$P^{\dagger}\left(v_{x}\right)=\gamma \cdot\left[P\left(v_{x}\right)+\sum_{v_{x i}} S S\left(v_{x}, v_{x c}\right) \cdot P\left(v_{x c}\right)\right]$$
where, $v_{x}$ and $v_{x c}$ are any two domain values corresponding to $V_{x} \in V_{S}$, so that $v_{x} \neq v_{x c} ; P\left(v_{x}\right)$ denotes the standard probability of $v_{x} ; \gamma$ is the normalization constant; and $S S\left(v_{x}, v_{x c}\right)$ denotes the semantic similarity between $v_{x}$ and $v_{x c}$

In order to estimate semantic similarity between any two concepts, semBnet needs the semantic knowledge base in the form of a semantic hierarchy (refer Fig.5.2). Assuming that a variable $X$ has semantic hierarchy available over its various concepts, the semantic similarity between any two of its concepts $x_{c_{1}}$ and $x_{c_{2}}$ is calculated as per the measure defined in [11] as follows.
$$S S\left(x_{c_{1}}, x_{c_{2}}\right)=e^{-\delta l} \cdot \frac{e^{\lambda d}-e^{-\lambda . d}}{e^{\lambda d}+e^{-\lambda . d}}$$
where, $d$ denotes the depth of subsumer (most immediate common ancestor) of the concept $x_{c_{1}}$ and $x_{c_{2}}$ in the semantic hierarchy; $l$ is the length of the shortest path between the concepts; $\lambda>0$ and $\delta \geq 0$ are control parameters that help to scale the contribution of $d$ and $l$, respectively. As mentioned in [11], usually, the $\lambda$ and $\delta$ are set to $0.6$ and $0.2$ respectively.

During conditional probability estimation, if the target variable $V_{x}$ does not have its semantic knowledge base available (i.e. $V_{x} \in V_{O}$ ) and neither of its parents has so (i.e. (Parent $\left.\left(V_{x}\right) \cap V_{S}\right)=\emptyset$ ), then the conditional probability distribution $P\left(V_{x} \mid\right.$ Parext $\left.\left(V_{x}\right)\right)$ is derived in the same way as that of standard BN. Otherwise, the available semantic information is utilized to estimate the conditional probabilities. Following are the three cases that can arise during conditional probability estimation in presence of domain semantics of at least one of the variables involved (target and/or its parents):
$\frac{\text { Case-I: } V_{x} \in V_{S} \text { and }\left(\text { Parents }\left(V_{x}\right) \cap V_{S}\right)=\emptyset:}{\text { Similar case arises for the variable } V_{S}^{4} \text { in Fig.5.3. }}$

## 统计代写|贝叶斯网络代写Bayesian network代考|semBnet-Based Prediction

Once the inferred probability distribution for the target/query variable is obtained, this is further processed to generate the predicted value of the variable. Considering the same example of rainfall prediction as described in the previous section, let $i n f e r_{R}^{s e m B n e t}$ is the semBnet inferred rainfall range corresponding to the highest probability estimate and infer standardBN is the standard Bayesian network inferred rainfall range corresponding to the highest probability estimate. Then $P^{\dagger}\left(i n f e r_{R}^{s e m} B n e t\right.$ $P\left(\right.$ infer ${ }{R}^{\text {standard } B N} \mid L U L C$, Elev, Lat $)=\max {i}\left{P\left(R_{i} \mid L U L C\right.\right.$, Elev, Lat $\left.)\right}$, where infer sembnet $=\left[L B_{R}^{\text {sem Bnet }}, U B_{R}^{\text {sem Bnet }}\right]$ and infer standard in $_{R}^{\text {s. }}=\left[L B_{R}^{\text {standard } B N}\right.$, $\left.U B_{R}^{\text {standard } B N}\right]$ (since the inferred values are in the form of ranges). Here $L B$ and $U B$ indicate the lower and upper bound of the range, respectively.
Then, the predicted value of Rainfall $\left(\mathrm{pred}{R}\right)$ is estimated as follows: $$\text { pred }{R}=\left[\frac{L B_{R}^{\text {sem Bnet }}+L B_{R}^{\text {standardBN }}}{2}, \frac{U B_{R}^{\text {sem } B \text { net }}+U B_{R}^{\text {standard } B N}}{2}\right]=\left[L B_{R}^{\text {pred }}, U B_{R}^{\text {pred }}\right]$$
In order to obtain a single value for Rainfall, one may use the mean of the predicted range: $\left(\frac{L B_{R}^{\text {pral }}+U B_{R}^{\text {preal }}}{2}\right)$.

In the following part of the chapter, we attempt to present a case study to validate the effectiveness of semBnet-based prediction model in the presence of domain knowledge over the variables.

## 统计代写|贝叶斯网络代写Bayesian network代考|Principle of semBnet

• 参数学习
• 推理生成
semBnet 通过结合领域知识扩展了标准贝叶斯网络分析，领域知识以语义层次 [5] 表示。在时空预测的情况下，语义层次是在空间域的各种概念上开发的，并作为知识库将域语义合并到标准贝叶斯分析中。

## 统计代写|贝叶斯网络代写Bayesian network代考|Parameter Learning

案例一： 在X∈在小号 和 ( 父母 (在X)∩在小号)=∅: 变量出现类似情况 在小号4 在图 5.3 中。

## 统计代写|贝叶斯网络代写Bayesian network代考|semBnet-Based Prediction

前 R=[大号乙R网络 +大号乙R默认BN 2,在乙R扫描仪 乙 网 +在乙R标准 乙ñ2]=[大号乙R前 ,在乙R前 ]

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。