### 统计代写|贝叶斯分析代写Bayesian Analysis代考|MAST90125

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯分析Bayesian Analysis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分析Bayesian Analysis代写方面经验极为丰富，各种代写贝叶斯分析Bayesian Analysis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|贝叶斯分析代写Bayesian Analysis代考|Accounting for Multiple Causes

Norman is not the only person whose chances of being late increase when there is a train strike. Martin is also more likely to be late, but Martin depends less on trains than Norman and he is often late simply as a result of oversleeping. These additional factors can be modeled as shown in Figure 7.3.

You should add the new nodes and edges using AgenaRisk. We also need the probability tables for each of the nodes Martin oversleeps (Table 7.3) and Martin late (Table 7.4).

The table for node Martin late is more complicated than the table for Norman late because Martin late is conditioned on two nodes rather than one. Since each of the parent nodes has two states, true and false (we are still keeping the example as simple as possible), the number of combinations of parent states is four rather than two.

If you now run the model and display the probability graphs you should get the marginal probability values shown Figure 7.4(a). In particular, note that the marginal probability that Martin is late is equal to $0.446$ (i.e. $44.6 \%$ ). Box $7.1$ explains the underlying calculations involved in this.

But if we know that Norman is late, then the probability that Martin is late increases from the prior $0.446$ to $0.542$ as shown in Figure 7.4(b). Box $7.1$ explains the underlying calculations involved.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Using Propagation to Make Special

When we enter evidence and use it to update the probabilities in the way we have seen so far we call it propagation. In principle we can enter any number of observations anywhere in the BN model and use propagation to update the marginal probabilities of all the unobserved variables.
This can yield some exceptionally powerful types of analysis. For example, without showing the computational steps involved, if we first enter the observation that Martin is late we get the revised probabilities shown in Figure 7.5(a).

What the model is telling us here is that the most likely explanation for Martin’s lateness is Martin oversleeping; the revised probability of a train strike is still low. However, if we now discover that Norman is also late (Figure 7.5(b)) then Train strike (rather than Martin oversleeps) becomes the most likely explanation for Martin being late. This particular type of (backward) inference is called explaining away (or sometimes called nonmonotonic reasoning). Classical statistical tools alone do not enable this type of reasoning and what-if analysis.

In fact, as even the earlier simple example shows, BNs offer the following benefits:

• Explicitly model causal factors – It is important to understand that this key benefit is in stark contrast to classical statistics whereby prediction models are normally developed by purely data-driven approaches. For example, the regression models introduced in Chapter 2 use historical data alone to produce equations relating dependent and independent variables. Such approaches not only fail to incorporate expert judgment in scenarios where there is insufficient data, but also fail to accommodate causal explanations. We will explore this further in Chapter $9 .$
• Reason from effect to cause and vice versa-A BN will update the probability distributions for every unknown variable whenever an observation is entered into any node. So entering an observation in an “effect” node will result in back propagation, that is, revised probability distributions for the “cause” nodes and vice versa. Such backward reasoning of uncertainty is not possible in other approaches.
• Reduce the burden of parameter acquisition-A BN will require fewer probability values and parameters than a full joint probability model. This modularity and compactness means that elicitation of probabilities is easier and explaining model results is made simpler.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Using Propagation to Make Special

• 显式建模因果因素——重要的是要了解，这一关键优势与经典统计形成鲜明对比，经典统计通常通过纯粹的数据驱动方法开发预测模型。例如，第 2 章介绍的回归模型仅使用历史数据来生成与因变量和自变量相关的方程。这种方法不仅无法在数据不足的情况下纳入专家判断，而且无法适应因果解释。我们将在本章中进一步探讨9.
• 从结果到原因的原因，反之亦然 – 每当将观察输入任何节点时，BN 都会更新每个未知变量的概率分布。因此，在“影响”节点中输入观察结果将导致反向传播，即修改“原因”节点的概率分布，反之亦然。这种对不确定性的反向推理在其他方法中是不可能的。
• 减少参数获取的负担——与完整的联合概率模型相比，BN 将需要更少的概率值和参数。这种模块化和紧凑性意味着概率的引出更容易，模型结果的解释也变得更简单。

## 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。