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

#### Doug I. Jones

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couryes-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
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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

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