### 统计代写|贝叶斯网络概率解释代写Probabilistic Reasoning With Bayesian Networks代考|BN model of multi-state systems

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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|贝叶斯网络概率解释代写Probabilistic Reasoning With Bayesian Networks代考|functional and dysfunctional analysis

A functional/dysfunctional approach can be used to build a BN without enumerating all functional/dysfunctional scenarios. A functional analysis like IDEF0 associated with a dysfunctional analysis, as proposed in [WEB 01, MUL 04, WEB 06, MED 13, MED 15], can serve to build a more readable structure. This approach is also well suited for multi-state systems.

Functional analysis of a system defines a model structure based on the functions achieved by the system. This analysis is interesting because it provides a model structure according to the levels of abstraction describing the functional architecture. Moreover, the system is not limited to the technical system, but can also include human or organizational levels [MED 11].

A function is achieved in a system if its environment provides the necessary input flows: operating conditions, operating supports, energy, orders, etc. Several input flows may contribute to the achievement of a function and the output flows represent the results of the function; thus, the pattern of a generic function is shown in Figure 3.4.

## 统计代写|贝叶斯网络概率解释代写Probabilistic Reasoning With Bayesian Networks代考|Non-deterministic CPT

For a multi-state system, the relation between the variable $y$ and its parents $x_{i}$ can be non-deterministic, as mentioned in section $2.4$. If the conditional probabilities defining $y$ are in the 0,1 interval then the CPT is deterministic. However, if these conditional probabilities are in the ] 0,1 [ interval, then the CPT is non-deterministic. As in the binary case, this kind of CPT means that the expert is not completely sure that the occurrence of $x_{i}$ leads to the occurrence of $y$. Non-deterministic CPT is encountered for several reasons: the relation between $x_{i}$ and $y$ is naturally non-determinist or some parents $\left(x_{i}\right)$ are missing from the model. The inability of an expert to define the relation between $x_{i}$ and $y$ with complete certainty is translated into a non-deterministic CPT.
Let us illustrate this concept using an industrial example. The Omega-20 methodology is dedicated to modeling human safety barrier (HSB) performance. As mentioned in [MIC 09], the assessment of the performance aims to determine the level of confidence in the barrier. The probability of efficiency of the HSB corresponds to a risk reduction factor of the critical event propagation. The HSB is Efficient or Not Efficient; if the HSB is Efficient, the propagation of the critical event is reduced by $100 \%$, and the occurrence of this accident becomes equal to 0 ; if the HSB is Not Efficient, the critical event propagation is not reduced, and the occurence of the accident is not affected by the HSB.

Moreover, the HSB is based on three steps: detection, diagnosis and action. Each of these steps has a performance classified as follows: 0,1 or 2 . The barrier acts to inhibit the critical event. As shown in Figure $3.11$, the HSBs reduce the probability that an event $x_{A}$ propagates its effect to the output $y$. If the detection is inefficient (confidence level 0 ), the diagnostic is of low quality and the action has a high stress level, and the event can propagate fully. If all these steps are at their best level, the efficiency probability is equal to 1 and the event $x_{A}$ cannot be propagated. When one of the steps is at level 1 , it divides the probability of the critical event propagation by 10 , and by 100 at level 2 . Then, if detection and diagnostic are at level 1 , then the HSB efficiency has a probability equal to $0.01$. Therefore, the efficiency probability takes values from $0.000001$ to 1 , as defined in Table 3.14.

## 统统计代写|贝叶斯网络概率解释代写Probabilistic Reasoning With Bayesian Networks代考|Industrial applications

The advantages of this modeling approach are particularly interesting when large systems are modeled. This section discusses an application of the modeling method to an industrial case to provide a model for decision-making in maintenance strategy evaluation. The maintenance process is fundamental to improving the availability and productivity of industrial systems. To control these performances, maintenance managers need to be able to choose a maintenance strategy and adequate resources to perform this strategy.

A decision-making application in maintenance is proposed by Medina-Oliva [MED 11]. The author formalizes a methodology to develop a model to evaluate and compare different maintenance strategies. The model required merges many complementary views of the system: a functional view of the system, a dysfunctional view of the system, an organization view of the maintenance department and the technical maintenance team, and the effectiveness of its action policy according to the logistics.It is impossible to formalize such a model as a monolithic set of interconnected variables. The model structure is based on fusion of the technical description of the functional view as described in the previous section and the integration of the human and organizational layer presented in Léges et al.’s PhD thesis [LÉG 08a, LÉG 09]. The methodology lies in the unification of different types of knowledge required for the construction of this model [MED 13, MED 15]. In this application, the BN reaches its limit; therefore, a probabilistic relational model (PRM) language is used to define the BN model and a specific inference algorithm is used to compute the probabilities in this very large model.

## 统统计代写|贝叶斯网络概率解释代写Probabilistic Reasoning With Bayesian Networks代考|Industrial applications

Medina-Oliva [MED 11] 提出了维护中的决策应用程序。作者正式制定了一种方法来开发模型来评估和比较不同的维护策略。所需的模型融合了系统的许多互补视图：系统的功能视图、系统功能失调的视图、维护部门和技术维护团队的组织视图，以及根据物流的行动策略的有效性。不可能将这样的模型形式化为一组相互关联的变量。模型结构基于前一节中描述的功能视图的技术描述的融合以及 Léges 等人的博士论文 [LÉG 08a, LÉG 09] 中提出的人员和组织层的集成。该方法在于统一构建该模型所需的不同类型的知识[MED 13，MED 15]。在这个应用中，BN 达到了它的极限；因此，使用概率关系模型 (PRM) 语言来定义 BN 模型，并使用特定的推理算法来计算这个非常大的模型中的概率。

## 广义线性模型代考

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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。