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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|Graphical Representation

A DBN models time explicitly by creating duplicates of the variables at each time point they are measured at. From the description above, our variables of interest are stuffiness (st), inside temperature (Tin), outside temperature (Tout) and a binary variable ( $m$ ) that models whether windows are open or not. If we disregard time, we could model them with a DAG like that in the top-left panel of Figure $4.1$.

Starting from this DAG, we can argue from common sense that $S t, T$ in and Tout change smoothly over time; so their values at a given time (say, $t_{1}$ ) should depend on the respective values 10 minutes earlier (say, $t_{0}$ ). The same is true for $m$, as we are not expecting to constantly open and close the windows every 10 minutes. We could model this intuition by taking the previous network, making a copy of the DAG for $t_{1}$ (while the original models the variables at $t_{0}$ ) and connecting each node in $t_{a}$ with the corresponding node in $t_{1}$ to obtain the DAG in the top-right panel. This new network encodes two assumptions: that the dependence structure between the nodes is the same at $t_{0}$ and $t_{1}$; and that variables at $t_{1}$ depend on those at $t_{0}$ but not on those at earlier time points.’ Implicitly, it also assumes that $t_{0}$ and $t_{1}$ are not instants, but rather averages over periods of time: if that were not the case it would be problematic to draw arcs between nodes in the same time point because that would imply they can affect each other without any time passing.

However, this second network has an important limitation: the marginal distributions of the nodes in $t_{a}$ are not required to be identical to the corresponding distributions in $t_{1}$. As a result, the network has a higher number of parameters and it will not be necessarily consistent when those parameters are estimated from data. (The same observation may have very different marginal probabilities at $t_{0}$ and $t_{1}$ !) So, instead of constructing a network modelling $t_{0}, t_{1}$ and the transition between $t_{0}$ and $t_{1}$, we prefer to assume that nodes in $t_{0}$ have the same distribution as those in $t_{1}$ and focus on modelling the transition between $t_{0}$ and $t_{1}$.

## 统计代写|贝叶斯网络代写Bayesian network代考|Learning a Dynamic Bayesian Network

Estimating the parameters of a DBN does not require any statistical method that is specific to DBNs: they make, after all, the same assumptions about the distributions of the nodes as the other types of BNs we explore in this book. Hence we refer the reader to Section $6.5 .2$ for a general overview of parameter learning and to Section $1.4$ for estimators that are specific to discrete BNs.

Learning the structure of the DAG that underlies a DBN, however, is different in a few specific ways from the general case (which we will cover in Section 6.5.1) and from what we saw in Section $1.5$ for discrete BNs. In order to capture the evolution of the variables over time, some nodes are measured at $t_{0}$ and some at $t_{1}$.

The two groups are treated differently when learning the DAG structure. Nodes in $t_{0}$ are not linked to each other by arcs because we do not care to model their probabilistic dependencies given how we will use the DBN. This means that we should blacklist all possible arcs between nodes in $t_{0}$ in order to make sure they are not considered during structure learning.

# 贝叶斯网络代考

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

DBN 通过在每个测量时间点创建变量的副本来显式地建模时间。根据上面的描述，我们感兴趣的变量是闷热 (st)、内部温度 (Tin)、外部温度 (Tout) 和二元变量 (米) 模拟窗户是否打开。如果我们不考虑时间，我们可以使用图左上方面板中的 DAG 对它们进行建模4.1.

## 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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