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

2022年7月21日

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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
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## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Thinking about Risk Using Causal Analysis

It is possible to avoid all these problems and ambiguities surrounding the term risk by considering the causal context in which both risks and opportunities happen. The key thing is that a risk (and, similarly, an opportunity) is an event that can be characterized by a causal chain involving (at least):

• The event itself
• At least one consequence event that characterizes the impact (so this will be something negative for a risk event and positive for an opportunity event)
• One or more trigger (i.e., initiating) events
• One or more control events that may stop the trigger event from causing the risk event (for risk) or impediment events (for opportunity)
• One or more mitigating events that help avoid the consequence event (for risk) or impediment event (for opportunity)
This approach (which highlights the symmetry between risks and opportunities) is shown in the example of Figure $3.10$.

In practice, we would not gain the full benefits of building a causal model, unless we combine the risk events and opportunity events in a single model as in Figure 3.11.

In many situations it is actually possible to use completely neutral language (neither risk nor opportunity) as explained in the example of Box 3.4.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Applying the Causal Framework to Armageddon

We already saw in Section 3.4.1 why the simple impact-based risk measure was insufficient for risk analysis in the Armageddon scenario. In particular, we highlighted:

1. The difficulty of quantifying (in isolation) the probability of the meteor strike.
2. The difficulty of quantifying (in isolation) the impact of a strike.
3. The lack of meaning of a risk measure that is a product of (isolated measures of) probability and impact.
To get round these problems we apply the causal framework to arrive at a model like the one shown in Figure $3.15$ (if we want to stick to events with just true or false outcomes then we can assume “Loss of life” here means something like loss of at least $80 \%$ of the world population).
The sensible risk measures that we are proposing are simply the probabilities you get from executing the BN model. Of course, before you can execute it you still have to provide some probability values (these are the strengths of the relationships). But, in contrast to the classic approach, the probability values you need to supply are relatively simple and they make sense. And you never have to define vague numhers for impact

To give you a feel of what you would need to do, in the BN for the uncertain event “Meteor strikes Earth” we still have to assign some probabilities. But instead of second guessing what this event actually means in terms of other conditional events, the model now makes it explicit and it becomes much easier to define the necessary conditional probability. What we need to do is define the probability of the meteor strike given each combination of parent states as shown in Figure $3.16 .$

# 贝叶斯分析代考

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Thinking about Risk Using Causal Analysis

• 事件本身
• 至少一个表征影响的后果事件（因此这对于风险事件是负面的，对于机会事件是正面的）
• 一个或多个触发（即启动）事件
• 一个或多个控制事件可以阻止触发事件引起风险事件（对于风险）或阻碍事件（对于机会）
• 一个或多个缓解事件有助于避免后果事件（对于风险）或阻碍事件（对于机会）
这种方法（强调风险和机会之间的对称性）如图示例所示3.10.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|Applying the Causal Framework to Armageddon

1. 量化（孤立地）流星撞击概率的难度。
2. （孤立地）量化罢工影响的难度。
3. 作为概率和影响的（孤立度量）产物的风险度量缺乏意义。
为了解决这些问题，我们应用因果框架来得出如图所示的模型3.15（如果我们想坚持只有真实或错误结果的事件，那么我们可以假设“失去生命”在这里意味着至少失去80%世界人口）。
我们提出的合理风险度量只是您从执行 BN 模型中获得的概率。当然，在执行它之前，您仍然必须提供一些概率值（这些是关系的优势）。但是，与经典方法相比，您需要提供的概率值相对简单且有意义。而且您永远不必为影响定义模糊的数字

## 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。