### 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|Motivations and Objectives

To date, a number of researches have been conducted to address the various issues in spatial time series prediction considering diverse domains of applications. The esteemed journals/transactions like ‘Spatial Data Science’ (Springer), ‘Geoinformatica’ (Springer), ‘Environmental Modelling and Software’ (Elsevier), ‘TSAS’ (ACM), ‘TGRS’ (IEEE), ‘JSTARS’ (IEEE), ‘GRSL’ (IEEE), ‘TGIS’ (Wiley and Sons), ‘IJGIS’ (Taylor and Francis Online), ‘TJSS’ (Taylor and Francis Online) etc. are worth mentioning as the typical source of the relevant published works. However, often it becomes difficult for the research beginners to get a unified view of the evolution of the related research area from those scattered literature. Hence, the development of this monograph is motivated not only by the current research thrust on spatial time series prediction but also by the need of mitigating such research gap by providing a compact material of study. Although the availability of books on spatial time series prediction is not very scarce $[2,4]$, majority of these focus either on describing the practical aspects of using methods built in commercial software like R [15], or on discussing the theoretical study of pure statistical approaches in this regard [3, 13]. Contrarily, the present monograph concentrates on recently developed prediction models based on Bayesian network which is one of the sig-

nificant members of the probabilistic reasoning family of computational intelligence techniques.

This monograph is primarily prepared for graduate students of Computer Science and Spatial Data Science. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. A basic background in probability theory is a pre-requisite for them. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph helpful. The open research problems as discussed with sufficient allusions can immensely help graduate researchers to identify topics of their own choice. The theoretical analyses and corresponding derivations presented along with the models may help them to better understand the working principles of the models. Moreover, the case studies on climatological and hydrological time series prediction, covered throughout the monograph, are expected to grow interest in the BN-based prediction models and to further explore their potentiality to solve problems from similar domains. The present monograph can also serve as an algorithmic cookbook for the relevant system developers. The monograph provides sufficient description of the parameter learning and inference generation process for each of the enhanced Bayesian network (BN) models, which can extensively ease the development of corresponding software packages. Eventually, this will also open up a huge opportunity to enrich the existing mathematical computing software, like MATLAB, R-tool etc., by integrating the developed packages.

## 统计代写|贝叶斯网络代写Bayesian network代考|Organization of the Monograph

The remainder of the monograph is organized as follows. Chap. 2 introduces the preliminary concepts of standard/classical Bayesian network (BN) along with its significance in modeling spatio-temporal dependency among domain variables. In the Chaps. 3-5, we provide thorough descriptions of three recently proposed enhanced models of Bayesian network that have been developed for dealing with different contexts of spatial time series prediction. The performance of each of these models is illustrated further through relevant case studies at the end of the chapters. Chap. 6 discusses on the issue of handling uncertainty in parameter learning process and introduces a few more variants of enhanced BN models having embedded fuzziness. A rigorous comparative analysis on computational complexity for all these enhanced BN models is presented in Chap. 7. Chapter 8 discusses on some additional prediction scenarios suitable for applying the enhanced BN models discussed in the previous chapters. Finally, Chap. 9 summarizes the whole monograph as well as opens up a number of research avenues for further exploring BN potentials to predict spatial time series data.

A dependence graph representing the order of traversal of the chapters is depicted in Fig. 1.4.

## 统计代写|贝叶斯网络代写Bayesian network代考|Basics of Bayesian Network

A Bayesian network is a graphical structure, more specifically, a Directed Acyclic Graph (DAG), which possesses the following characteristics:

• it is a probabilistic graphical model representing a set of random variables, and their conditional dependencies via the DAG;
• the nodes in the DAG represent a set of random variables, $X=\left{X_{1}, X_{2}, \ldots, X_{i}\right}$, in the domain of interest. The variables may be observable quantities, latent variables, unknown parameters, or hypotheses;
• the set of directed edges/links, each connecting a pair of nodes, $X_{i} \rightarrow X_{j}$, represents direct dependency between the variables, and $X_{i}$ is treated as the parent of $X_{j}$. The nodes, which are not connected, represent variables that are conditionally independent of each other;
• each node $\mathrm{X}$ is associated with a conditional probability distribution $P\left(X_{i} \mid\right.$ Parents $\left.\left(X_{i}\right)\right)$. This quantifies the effect of the parents on the node.

A typical example of standard Bayesian network architecture is shown in the Fig. 2.1. As shown in the figure, the network is composed of five nodes, corresponding to five different variables, namely Malware $(M)$, Power Failure $(W)$, OS Failure $(S)$, Hardware Failure $(H)$, and System Crash $(C)$. The causal dependencies among the variables can be interpreted as follows: both Malware and Power Failure can cause OS Failure; Power Failure can also cause Hardware Failure; finally, both OS Failure and Hardware Failure can cause System Crash. The probability distributions for each of these variables are represented through the tables beside the corresponding nodes.

## 统计代写|贝叶斯网络代写Bayesian network代考|Basics of Bayesian Network

• 它是一个概率图形模型，表示一组随机变量，以及它们通过 DAG 的条件依赖关系；
• DAG 中的节点代表一组随机变量，X=\left{X_{1}, X_{2}, \ldots, X_{i}\right}X=\left{X_{1}, X_{2}, \ldots, X_{i}\right}，在感兴趣的领域。变量可能是可观察量、潜在变量、未知参数或假设；
• 一组有向边/链接，每个连接一对节点，X一世→Xj, 表示变量之间的直接依赖关系，并且X一世被视为父级Xj. 未连接的节点代表条件独立的变量；
• 每个节点X与条件概率分布相关联磷(X一世∣父母(X一世)). 这量化了父节点对节点的影响。

## 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。