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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|Bayesian Networks

A BN is a directed acyclic graph composed of nodes and arcs [27]. Nodes represent random variables, and directed arcs between pairs of nodes denote dependencies between the variables. Conditional probability distribution is specified at each node that has parents, while prior probability is specified at node that has no parents. The nodes $\left(X_{1}, \ldots, X_{N}\right)$ in the network are labeled by related random variables. Assuming $\mathrm{Pa}\left(X_{i}\right)$ is the parent node of $X_{i}$ in the model, the conditional probability distribution of $X_{i}$ is denoted by $P\left(X_{i} \mid \mathrm{Pa}\left(X_{i}\right)\right)$. The joint probability distribution $P\left(X_{1}, \ldots, X_{N}\right)$ can be written as Eq. (1).
$$P\left(X_{1}, \ldots, X_{N}\right)=\prod_{X i \in\left{X_{1}, \ldots, X_{N}\right}} P\left(X_{i} \mid \mathrm{Pa}\left(X_{i}\right)\right)$$
BN allows both forward (or predictive) analysis and backward (diagnostic) analysis [28], where the posterior probability of any set of variables can be calculated.
Inference in $\mathrm{BNs}$ is to calculate the probability of each node when other variables are known. Bayesian theorem is used to compute conditional probabilities. Given the variable $Y$, the conditional probability of $X$ is given by
$$P(X \mid Y)=\frac{P(X) P(Y \mid X)}{P(Y)}$$

## 统计代写|贝叶斯网络代写Bayesian network代考|GO-FLOW Methodology

The GO-FLOW method is a success-oriented system analysis technique and capable of evaluating system reliability. It uses a set of standardized operators to describe the logic operation, interaction, and combination of physical equipment. The operators are used to model function or failure of physical equipments, logical gates and a signal generator. As shown in Fig. 1, fourteen different types of operators are defined.
A system is modeled by selecting operators and interrelating their inputs and outputs. Consisting of operators and signal lines, GO-FLOW chart represents the engineering function of the system. The signal lines identify the inputs and outputs to the operators, with which the operators are connected. Signals denote some physical quantity or information, for example, the electric current in the conductor, a command signal, etc. A quantity defined as “intensity” is associated with a signal line. Generally, the intensity represents the probability of signal existence. When a signal is a subinput signal to the type-35, $-37$, or $-38$ operator, the intensity is used for denoting a time interval.

A finite number of discrete time values are required for expressing the system operation sequence. The values do not necessarily denote real time but correspond to it and represent an ordering. The total number of time points is a user-defined parameter.

The GO-FLOW method has the following features: (a) The GO-FLOW chart corresponds to the physical layout of a system and is easy to construct and validate,
(b) alterations and updates to a GO-FLOW chart are readily finished, and (c) GOFLOW includes all possible system operational states.

# 贝叶斯网络代考

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

BN 是由节点和弧组成的有向无环图 [27]。节点表示随机变量，节点对之间的有向弧表示变量之间的依赖关系。在每个具有父节点的节点处指定条件概率分布，而在 没有父节点的节点处指定先验概率。节点 $\left(X_{1}, \ldots ， X_{N}\right)$ 在网络中由相关的随机变量标记。假设 $P a\left(X_{i}\right)$ 是的父节点 $X_{i}$ 在模型中，条件概率分布 $X_{i}$ 表示为 $P\left(X_{i} \mid P a\left(X_{i}\right)\right)$. 联合概率分布 $P\left(X_{1}, \ldots ， X_{N}\right)$ 可以写成方程。(1)。
\left 的分隔符缺失或无法识别
$\mathrm{BN}$ 允许前向 (或预测) 分析和后向（诊断）分析 [28]，其中可以计算任何一组变量的后验概率。

$$P(X \mid Y)=\frac{P(X) P(Y \mid X)}{P(Y)}$$

## 统计代写|贝叶斯网络代写Bayesian network代考|GO-FLOW Methodology

GO-FLOW 方法是一种面向成功的系统分析技术，能够评估系统的可靠性。它使用一组标准化的算子来描述物理设备的逻辑操作、交互和组合。运算符用于模拟物理设备、逻辑门和信号发生器的功能或故障。如图 1 所示，定义了十四种不同类型的算子。

GO-FLOW 方法具有以下特点： (a) GO-FLOW 图对应于系统的物理布局，易于构建和验证，
(b) GO-FLOW 图的更改和更新很容易完成， (c) GOFLOW 包括所有可能的系统运行状态。

## 有限元方法代写

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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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