统计代写|贝叶斯分析代写Bayesian Analysis代考|STAT4102

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| Do Not Forget the Importance of the Variance in the TNormal Distribution

The variance captures our uncertainty about the weighted function. Because the TNormal for ranked nodes is always in the range $[0,1]$ any variance above $0.5$ would be considered very high (you should try it out on a simple weighted mean example). You may need to experiment with the variance to get it just right.

In each of the previous examples the variance was a constant, but in many situations the variance will be dependent on the parents. For example, consider the $\mathrm{BN}$ in Figure $9.40$ that is clearly based on a definitional idiom.

In this case system quality is defined in terms of the quality of two subsystems $S 1$ and $S 2$. It seems reasonable to assume all nodes are ranked and that the NPT for System quality should be a TNormal whose mean is a weighted mean of the parents. Assuming that the weights of $S 1$ and $S 2$ are equal we therefore define the mean of the TNormal as wmean $(S 1, S 2)$.

However, it also seems reasonable to assume that the variance depends on the difference between the two subsystem qualities. Consider, for example these two scenarios for subsystems $S 1$ and $S 2$ :

1. Both $S 1$ and $S 2$ have “medium” quality.
2. $S 1$ quality is “very high,” while $S 2$ quality is “very low.”
If the variance in the TNormal expression is fixed at, say $0.1$, then the System Quality in both scenarios 1 and 2 will be the same-as is shown in Figure 9.41(a) and (b). Specifically, the system quality in both cases is medium but with a lot of uncertainty.

However, it seems logical to assume that there should be less uncertainty in scenario 1 (when both subsystems have the same, medium, quality) than in scenario 2 (when both subsystems have very different levels of quality). To achieve the required result we therefore have to ensure that the variance in the TNormal expression is a function of the difference in subsystem qualities. Setting the variance as abs(S1-S2)/5 produces the required result as shown in Figure 9.41(c) and (d).

The use of a variable variance also enables us to easily implement the measurement idiom in the case where all the nodes of the idiom are ranked. This is explained in Box 9.12. The special case of indicator nodes is shown in Box 9.13.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Elicitation Protocols and Cognitive Biases

We are aiming to build a scientific model, so open, factual, and honest discussion of the risks, our beliefs (i.e., theories) about how they interrelate, and what the probabilities are is of the utmost importance. The elicitor (the modeler/risk analyst) and the elicitee (the subject matter expert) must be mutually respectful of each other’s professionalism, skills, and objectives. Attributes of a good elicitation protocol involve elicitors making an effort to understand subject matter sufficiently to probe and challenge discussion in order to allow experts to sharpen and refine thinking. Similarly, more accurate probabilities are elicited when people are asked for reasons for them, but the BN structure supplies some or all of this, thus making this easier than when asking for probabilities alone. Without these prerequisites the elicitation exercise will be futile.

Some practical advice on how to elicit numbers from experts is provided in O’Hagan et al (2006). Box $9.14$ provides some examples of what has been used, based primarily on Spetzler and von Holstein 1975 (also known as the Stanford Elicitation Prototcol).

There is plenty of advice on how not to perform elicitation from the field of cognitive psychology as pioneered by Kahneman and colleagues (1982). A summary (by no means exhaustive) of the well-known biases is listed next and we recommend that these be presented and discussed with experts as part of any pre-elicitation training:

• Ambiguity effect-Avoiding options for which missing information makes the probability seem unknown.
• Attentional bias-Neglecting relevant data when making judgments of a correlation or association.
• Availability heuristic-Estimating what is more likely by what is more available in memory, which is biased toward vivid, unusual, or emotionally charged examples.
• Base rate neglect-Failing to take account of the prior probability. This was at the heart of the common fallacious reasoning in the Harvard medical study described in Chapter 2 . It is the most common reason for people to feel that the results of Bayesian inference are nonintuitive.
• Bandwagon effect – Believing things because many other people do (or believe) the same. Related to groupthink and herd behavior.
• Confirmation bias-Searching for or interpreting information in a way that confirms one’s preconceptions.
• Déformation professionnelle-Ignoring any broader point of view and seeing the situation through the lens of one’s own professional norms.

统计代写|贝叶斯分析代写贝叶斯分析代考|不要忘记方差在t正态分布中的重要性

1. $S 1$和$S 2$都是中等质量。
2. $S 1$质量“非常高”，而$S 2$质量“非常低”。如果TNormal表达式中的方差固定在，比如$0.1$，那么在场景1和场景2中的系统质量将是相同的，如图9.41(a)和(b)所示。具体地说，在这两种情况下，系统质量是中等的，但有很大的不确定性然而，假设场景1(当两个子系统具有相同的中等质量时)的不确定性应该比场景2(当两个子系统具有非常不同的质量水平时)的不确定性更低似乎是合乎逻辑的。因此，为了达到所需的结果，我们必须确保TNormal表达式中的方差是子系统质量差异的函数。将方差设为abs(S1-S2)/5会产生如图9.41(c)和(d)所示的结果变量方差的使用还使我们能够轻松地实现度量习惯用法，在这种情况下，习惯用法的所有节点都是排序的。这将在框9.12中解释。指示节点的特殊情况在框9.13中显示
统计代写|贝叶斯分析代写贝叶斯分析代考|启发式协议和认知偏差
我们的目标是建立一个科学的模型，所以公开、实事求是和诚实地讨论风险，我们的信念(即理论)是如何相互联系的，以及概率是什么是最重要的。激发者(建模师/风险分析师)和被激发者(主题专家)必须相互尊重对方的专业知识、技能和目标。一个好的诱导协议的属性包括诱导者努力充分理解主题，以探索和挑战讨论，以便让专家们提高和精炼思维。类似地，当人们被问及其原因时，会引出更准确的概率，但BN结构提供了部分或全部这些，因此比单独询问概率更容易。没有这些先决条件，启发练习将是徒劳的O’Hagan等人(2006)就如何从专家那里引出数字提供了一些实用的建议。Box $9.14$提供了一些已经使用的例子，主要基于Spetzler和von Holstein 1975(也称为斯坦福启发协议)。Kahneman和他的同事(1982)在认知心理学领域率先提出了很多关于如何不进行诱导的建议。下面是对众所周知的偏见的总结(并非详尽无遗)，我们建议将这些偏见作为任何预诱导培训的一部分与专家讨论:
• 歧义效应—避免信息缺失使概率看起来未知的选项。注意偏差-在对相关或关联做出判断时忽略相关数据。
• 可用性启发式-通过记忆中更多的可用性来估计什么更有可能发生，这偏向于生动的、不寻常的或情绪化的例子。
• 基准率忽略-未考虑先验概率。这就是第二章中描述的哈佛医学研究中常见谬误推理的核心。人们觉得贝叶斯推断的结果是非直观的，这是最常见的原因。
• 从众效应-相信一些事情，因为许多其他人也这么做(或相信)。与群体思维和从众行为有关。
• 确认偏误——以一种证实某人先入为主的方式搜索或解释信息。
• Déformation professionnelle-忽略任何更广泛的观点，通过自己的专业规范来看待情况

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STATS3023

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|Hints and Tips When Working with Ranked Nodes and NPTs

We have found that the set of weighted functions (i.e., WMEAN, WMIN, WMAX, and MIXMINMAX) is sufficient to generate almost any ranked node NPT in practice where the ranked node’s parents are all ranked.

In cases where the weighted function does not exactly capture the requirements for the node’s $\mathrm{NPL}^{\prime}$ it is usually possible to get to what you want by manually tweaking the NPT that is generated by a weighted function. For example, Figure $9.37$ shows a part of the table that is automatically generated for the node $Y$ as specified in Figure 9.31.

You will note that the probability of $Y$ being “very high” when both parents are “very low” is very close to 0 but not equal to 0 . If you really want this probability to be 0 then you can simply enter 0 manually into that cell.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Exploit the Fact That a Ranked Node Parent Has an Underlying Numerical Scale

In many real-world models you will find that nodes that are not ranked nodes will have one or more parents that are ranked. In such situations you can exploit the underlying numerical property of the ranked node parent to define the NPT of the child node. For example, it makes sense to extend the model of Figure $9.35$ by adding a Boolean node called Release Product? which is true when the product has been sufficiently well tested to be released and false otherwise. The extended model is shown in Figure 9.38.

We could as usual define the NPT for the new Boolean node manually (it has 10 entries). But it makes much more sense and is far simpler to exploit the fact that the node $Y$ has an underlying numerical value between 0 and 1. Since we have a 5-point scale we know that if $Y$ is above $0.5$ then the quality is at least “medium.” If the value is $0.7$ then the quality is in the middle of the “high” range. So, suppose that previous experience suggests that testing effectiveness needs to be “high” in order for the product to be released without too many problems. Then we can simply define the NPT of the node Release product? by the expression:
if $(\mathrm{Y}>0.7$, “True”, “False”).
The effect of running the resulting model with some observations is shown in Figure 9.39.

.

统计代写|贝叶斯分析代写贝叶斯分析代考|利用分级节点父节点具有底层数值尺度的事实

if $(\mathrm{Y}>0.7$， “True”， “False”)。运行结果模型和一些观察结果的效果如图9.39所示

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

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|Weighted Averages

A common simple approach to quantitative risk assessment is to use a weighted average score to combine risks and produce an overall “risk score” as shown in Table 9.7. This is purely arithmetical and is easily implemented in a spreadsheet, such as Excel. Here we have identified three risks to a project: Risk $\mathrm{A}$, Risk $\mathrm{B}$ and Risk $\mathrm{C}$ with respective probabilities $10 \%, 20 \%$ and $80 \%$ and “weights” 3,2 , and 1 . This produces an overall weighted average risk score of $25 \%$.

As we saw in Chapter 3, this is the “risk register” approach that can be viewed as the extension of the simple approach to risk-assessment in which we define risk as probability times impact. Specifically, the impacts are viewed as relative “weights.”

For all of the reasons discussed in Chapter 3 we do not recommend this approach to risk assessment, but there may be many reasons why we would want to incorporate weighted averages into a BN. For example, we might wish to use a weighted average as a score to determine which new car to buy based on criteria such as price, quality, and delivery time. Although the weighted average is deterministic (and therefore can be computed in Excel) the values for the criteria could be based on a range of uncertain factors and relationships that require a BN model in which the weighted average is just a component.

Fortunately, it is possible to replicate weighted averages (using the same example probabilities and weights as Table 9.7) in a BN as shown in Figure 9.23.

Each of the risk factors is represented by a Boolean node whose “probability” is simply specified as the “True” value in the NPTso, for example, since Risk A has probability $10 \%$ we set its NPT as “True” $=10 \%$. The Risk Score node is also Boolean but it makes sense to replace the labels “False” and “True” with “Low” and “High,” respectively. The key to ensure we can replicate the weighted average calculation is to introduce the labelled node Weights whose states correspond to the three risk node weights. The normalised weights are used in the NPT for this node.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Alternative Weighted Functions

The weighted mean is not the only natural function that could be used as the mean of the TNormal ranked node NPTs. Suppose, for example, that in Figure $9.26$ we replace the node Quality of Testing Process with the node Testing Effort as shown in Figure 9.35.
In this case we elicit the following information:

• When $X_1$ and $X_2$ are both “very high” the distribution of $Y$ is heavily skewed toward “very high.”
• When $X_1$ and $X_2$ are both “very low” the distribution of $Y$ is heavily skewed toward “very low.”
• When $X_1$ is very low and $X_2$ is “very high” the distribution of $Y$ is centered toward “very low.”
• When $X_1$ is very high and $X_2$ is “very low” the distribution of $Y$ is centered toward “low.”

Intuitively, the expert is saying here that, for testing to be effective, you need not just to have good people but also to put in the effort. If either the people or the effort is insufficient, then the result will be poor. However, really good people can compensate to a small extent for lack of effort.
A simple weighted mean for $Y$ will not produce an NPT to satisfy these elicited requirements (you can try it out by putting in different weights; you will never be able to satisfy both of the last two elicited constraints). Informally, $Y$ ‘s mean is something like the minimum of the parent values, but with a small weighting in favor of $X_1$. The necessary function, which we call the weighted min function (WMIN), is what is needed in this case. The general form of this function (together with analogous WMAX and the mixture function MIXMINMAX) is shown in Box 9.11. You need not know the details because the function is built into AgenaRisk, so it is sufficient to know what the effect of the function is with different values.

统计代写|贝叶斯分析代写贝叶斯分析代考|备选加权函数

• 当$X_1$和$X_2$都是“非常高”时，$Y$的分布严重偏向于“非常高”。当$X_1$和$X_2$都是“非常低”时，$Y$的分布严重偏向于“非常低”。
• 当$X_1$非常低，$X_2$非常高时，$Y$的分布以“非常低”为中心。
• 当$X_1$非常高，$X_2$是“非常低”时，$Y$的分布以“低”为中心。

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STAT4102

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|The Crucial Independence Assumptions

Take a look again at the BN model of Figure $7.3$ and the subsequent calculations we used. Using the terminology of Chapter 5 what we have actually done is use some crucial simplifying assumptions in order to avoid having to work out the full joint probability distribution of:
(Norman late, Martin late, Martin oversleeps, Train strike) We will write this simply as $(N, M, O, T)$
For example, in calculating the marginal probability of $\operatorname{Martin}$ late $(M)$ we assumed that $M$ was dependent only on Martin oversleeps $(O)$ and Train strike $(T)$. The variable Norman late $(N)$ simply did not appear in the equation because we assume that none of these variables are directly dependent on $N$. Similarly, although $M$ depends on both $O$ and $T$, the variables $O$ and $T$ are independent of each other.

These kind of assumptions are called conditional independence assumptions (we will provide a more formal definition of this later). If we were unable to make any such assumptions then the full joint probability distribution of $(N, M, O, T)$ is (by the chain rule of Chapter 5)
$$P(N, M, O, T)=P(N \mid M, O, T) P(M \mid O, T) P(O \mid T) P(T)$$
However, because $N$ directly depends only on $T$ the expression $P(N \mid M, O, T)$ is equal to $P(N \mid T)$, and because $O$ is independent of $T$ the expression $P(O \mid T)$ is equal to $P(O)$.
Hence, the full joint probability distribution can be simplified as:
$$P(N, M, O, T)=P(N \mid T) P(M \mid O, T) P(O) P(T)$$
and this is exactly what we used in the computations.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Structural Properties of BNs

In $\mathrm{BNs}$ the process of determining what evidence will update which node is determined by the conditional dependency structure. The main formal area of guidance for building sensible BN structures therefore requires some understanding of different types of relationships between variables and the different ways these relationships are structured.

Generally we are interested in the following problem. Suppose that variable $A$ is linked to both variables $B$ and $C$. There are three different ways the links can be directed as shown in Figure 7.8. Although $B$ and $C$ are not directly linked, under what conditions in each case are $B$ and $C$ independent of $A$ ?

Knowing the answer to this question enables us to determine how to construct appropriate links, and it also enables us to formalize the different notions of conditional independence that we introduced informally in Chapter $6 .$

The three cases in Figure $7.8$ are called, respectively, serial, diverging, and converging connections. We next discuss each in turn.

Consider the example of a serial connection as shown in Figure 7.9. Suppose we have some evidence that a signal failure has occurred $(B)$. Then clearly this knowledge increases our belief that the train is delayed $(A)$, which in turn increases our belief that Norman is late $(C)$. Thus, evidence about $B$ is transmitted through $A$ to $C$ as is shown in Figure 7.10.

However, now suppose that we know the true status of $A$; for example, suppose we know that the train is delayed. Then this means we have hard evidence for A (see Box $7.5$ for an explanation of what hard and uncertain evidence are and how they differ).

统计代写|贝叶斯分析代写Bayesian Analysis代考|The Crucial Independence Assumptions

$$P(N, M, O, T)=P(N \mid M, O, T) P(M \mid O, T) P(O \mid T) P(T)$$

$$P(N, M, O, T)=P(N \mid T) P(M \mid O, T) P(O) P(T)$$

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

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

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STATS3023

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|Second-Order Probability

Recall the Honest Joe’s and Shady Sam’s example we encountered in Chapter 4 (Box 4.7). In that example we expressed a belief in the chance of the die being fair or not, as a probability, while being aware that the fairness is also expressed as a probability. At first glance this looks very odd indeed since it suggests we are measuring a “probability about a probability.” We call this a second-order probability. If we think of the fairness of the die, and its chance of landing face up on a 6 , as a property of the die and how it is thrown, then we are expressing a degree of belief in the chance of the die having a given value. This is no different from expressing a degree of belief in a child reaching a given height when they mature in the sense that height and chance are both unknown properties of a thing that is of interest to us.
Example $6.10$ shows how we might model such second-order probabilities in practice.

Let us assume someone has smuggled a die out of either Shady Sam’s or Honest Joe’s, but we do not know which casino it has come from. We wish to determine the source of the die from (a) a prior belief about where the die is from and (b) data gained from rolling the die a number of times.

We have two alternative hypotheses we wish to test: Joe (“die comes from Honest Joe’s”) and Sam (“die comes from Shady Sam’s”). The respective prior probabilities for these hypotheses are:
$$P(\text { Joe })=0.7 \quad P(\text { Sam })=0.3$$
This is justified by the suspicion that our smuggler may be deterred by the extra personal risk in smuggling a die from Shady Sam’s compared with Honest Joe’s.

The data consists of 20 rolls of the die, observing there was one “6” and nineteen “not 6 ” results. So, we need to compute the likelihoods $\mathrm{P}($ Joe I data) and $\mathrm{P}($ Sam I data) and combine these (by Bayes theorem) with our prior beliefs about the hypotheses to get our posterior beliefs. To compute the likelihoods, recall that we believed that a die from Honest Joe’s was fair, with a chance of a 6, $p=1 / 6$, and from Shady Sam’s it was unfair, say, $p=1 / 12$. We can use these assumptions with the Binomial distribution to generate the likelihoods we need for the data, $X$ successes in 20 trials, given each of our hypotheses:
$$P(X=x \mid p, 20)=\left(\begin{array}{l} 20 \ x \end{array}\right) p^x(1-p)^{20-x}$$
The results are shown in Table 6.4. Notice that we are expressing beliefs in hypotheses that are equivalent to beliefs about probabilities, in this case
$$P(\text { Joe })=P(p=1 / 6)$$
and
$$P(\text { Sam })=P(p=1 / 12)$$
From Table $6.4$ we can see that we now should favor the hypothesis that the die was sourced from Shady Sam’s rather than Honest Joe’s. This conclusion reverses our prior assumption (which had favored Honest Joe’s).

统计代写|贝叶斯分析代写Bayesian Analysis代考|A Very Simple Risk Assessment Problem

Since it is important for Norman to arrive on time for work, a number of people (including Norman himself) are interested in the probability that he will be late. Since Norman usually travels to work by train, one of the possible causes for Norman being late is a train strike. Because it is quite natural to reason from cause to effect we examine the relationship between a possible train strike and Norman being late. This relationship is represented by the causal model shown in Figure $7.1$ where the edge connects two nodes representing the variables “Train strike” $(T)$ to “Norman late” $(N)$.

It is obviously important that there is an edge between these variables since $T$ and $N$ are not independent (using the language of Chapter 5); common sense dictates that if there is a train strike then, assuming we know Norman travels by train, this will affect his ability to arrive on time. Common sense also determines the direction of the link since train strike causes Norman’s lateness rather than vice versa.

To ensure the example is as simple as possible we assume that both variables are discrete, having just two possible states: true and false.
Let us assume the following prior probability information:

1. The probability of a train strike is $0.1$ (and therefore the probability of no train strike is 0.9). This information might be based on some subjective judgment given the most recent news or it might be based on the recent frequency of train strikes (i.e. one occurring about every 10 days). So the prior probability distribution for the variable “Train strike” is as shown in Table 7.1.
2. The probability Norman is late given that there is a train strike is $0.8$ (and therefore the probability Norman is not late given that there is a train strike is $0.2$ ). The probability Norman is late given that there is not a train strike is $0.1$ (and therefore the probability Norman is not late given that there is not a train strike is 0.9). So, the (conditional) probability distribution for “Norman late” given “Train strike” is as shown in Table $7.2$.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Second-Order Probability

$$P(\text { Joe })=0.7 \quad P(\text { Sam })=0.3$$

$$P(X=x \mid p, 20)=(20 x) p^x(1-p)^{20-x}$$

$$P(\text { Joe })=P(p=1 / 6)$$

$$P(\mathrm{Sam})=P(p=1 / 12)$$

统计代写|贝叶斯分析代写Bayesian Analysis代考|A Very Simple Risk Assessment Problem

1. 火车撞车的概率是0.1（因此没有火车撞击的概率是 0.9）。该信息可能基于给定最新消息的一些主观判断，也可能基于最近的火车罢工频率（即大约每 10 天发生一次）。因此，变量“火车罢工”的先验概率分布如表 7.1 所示。
2. 鉴于火车罢工，诺曼迟到的概率是0.8（因此，鉴于火车罢工，诺曼不迟到的概率是0.2）。鉴于没有火车罢工，诺曼迟到的概率是0.1（因此，鉴于没有火车罢工，诺曼不迟到的概率是 0.9）。因此，给定“火车罢工”的“诺曼晚点”的（条件）概率分布如表所示7.2.

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STAT4102

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|Probability Notation Where There Are Different

Consider the experiment of rolling two fair dice. There are many different outcomes of interest for this experiment including the following:

• The sum of the two dice rolled (let’s call this outcome $X$ ).
The highest number die rolled (let’s call this outcome $Y$ ).
These two different outcomes of interest have different sets of elementary events.
Outcome $X$ has eleven elementary events: 2,3,4,5,6,7,8,9,10, 11, 12 .
• Outcome $Y$ has six elementary events: 1, 2, 3, 4, 5, 6 .
If we are not careful about specifying the particular outcome of interest for the experiment, then there is the potential to introduce genuine ambiguity when calculating probabilities.

For example, consider the elementary event ” 2 .” What is the probability of observing this event for this experiment? In other words what is $P(2)$ ? The answer depends on whether we are considering outcome $X$ or outcome $Y$ :

• For outcome $X$, the probability $P(2)$ is $1 / 36$ because there are 36 different ways to roll two dice and only one of these, the roll $(1,1)$, results in the sum of the dice being 2 .
• For outcome $Y$, the probability $P(2)$ is $1 / 12$ because of the 36 different ways to roll two dice there are three ways, the rolls $(1,2),(2,1)$ and $(2,2)$, that result in the highest number rolled being 2 .
Because of this ambiguity it is common practice, when there are different outcomes of interest for the same experiment to include some notation that identifies the particular outcome of interest when writing down probabilities. Typically, we would write $P(X=2)$ or $P(Y=2)$ instead of just $P(2)$.

The notation extends to events that comprise more than one elementary event. For example, consider the event $E$ defined as “greater than 3”:

• For outcome $X$, the event is $E$ is equal to ${4,5,6,7,8,9,10,11,12}$.
• For outcome $Y$, the event is $E$ is equal to ${4,5,6}$.
We calculate the probabilities as
• For event $X, P(E)=11 / 12$.
• For event $Y, P(E)=3 / 4$.
Typically we would write $P(X=E)$ or $P(X \geq 3)$ for the former and $P(Y=E)$ or $P(Y \geq 3)$ for the latter.
In this example the outcomes $X$ and $Y$ can be considered as variables whose possible values are their respective set of elementary events. In general, if there is not an obviously unique outcome of interest for an experiment, then we need to specify each outcome of interest as a named variable and include this name in any relevant probability statement.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Probability Distributions

Consider the experiment of selecting a contractor to complete a piece of work for you. We are interested in the outcome “quality of the contractor.” Since, as discussed in Box 5.5, this is just one of many possible outcomes of interest for this experiment (others might be price of contractor, experience of contractor, etc.) it is safest to associate a variable name, say $Q$, with the outcome “quality of the contractor.” Let us assume that the set of elementary events for $Q$ is {very poôr, poōr, averāge, good, very good}.

On the basis of our previous experience with contractors, or purely based on subjective judgment, we might assign the probabilities to these elementary events for $Q$ as shown in the table of Figure 5.2(a). Since the numbers are all between 0 and 1 , and since they sum to 1 , this assignment is a valid probability measure for $Q$ (i.e., for the experiment with outcome $Q$ ) because it satisfies the axioms.

A table like the one in Figure 5.2(a), or equivalent graphical representations like the ones in Figure 5.2(b) and Figure 5.2(c), is called a probability distribution. In general, for experiments with a discrete set of elementary events:There is a very common but somewhat unfortunate notation for probability distributions. The probability distribution for an outcome such as $Q$ of an experiment is often written in shorthand as simply: $P(Q)$. If there was an event referred to as $Q$ then the expression $P(Q)$ is ambiguous since it refers to two very different concepts. Generally it will be clear from the context whether $P(Q)$ refers to the probability distribution of an outcome $Q$ or whether it refers to the probability of an event $Q$.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Probability Notation Where There Are Different

• 掷出的两个骰子的总和（我们称这个结果为X）。
掷出的最高点数（我们称这个结果为是的）。
这两种不同的兴趣结果具有不同的基本事件集。
结果X有十一个基本事件：2,3,4,5,6,7,8,9,10,11,12。
• 结果是的有六个基本事件：1、2、3、4、5、6。
如果我们在指定实验感兴趣的特定结果时不小心，那么在计算概率时就有可能引入真正的歧义。

• 对于结果X, 概率磷(2)是1/36因为掷两个骰子有 36 种不同的方法，而其中只有一种，掷骰子(1,1)，结果骰子的总和为 2 。
• 对于结果是的, 概率磷(2)是1/12因为掷两个骰子有 36 种不同的方式，所以有三种方式，掷骰子(1,2),(2,1)和(2,2)，这导致滚动的最高数字为 2 。
由于这种模糊性，通常的做法是，当同一实验有不同的感兴趣结果时，在写下概率时包含一些标识感兴趣的特定结果的符号。通常，我们会写磷(X=2)或者磷(是的=2)而不仅仅是磷(2).

• 对于结果X, 事件是和等于4,5,6,7,8,9,10,11,12.
• 对于结果是的, 事件是和等于4,5,6.
我们计算概率为
• 活动X,磷(和)=11/12.
• 活动是的,磷(和)=3/4.
通常我们会写磷(X=和)或者磷(X≥3)对于前者和磷(是的=和)或者磷(是的≥3)对于后者。
在这个例子中，结果X和是的可以被认为是变量，其可能值是它们各自的基本事件集。一般来说，如果一个实验没有明显独特的感兴趣的结果，那么我们需要将每个感兴趣的结果指定为一个命名变量，并将这个名称包含在任何相关的概率陈述中。

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STATS3023

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|When Frequentist and Subjective Approaches Merge

Consider the following two statements:

1. There is a $50.9 \%$ chance that a new born baby in the United Kingdom is a girl.
2. There is a $5 \%$ chance of the Spurs winning the FA Cup next year.
On the surface there seems to be no doubt that statement 1 is explained by a frequentist argument: Over the last 100 years $50.9 \%$ of all births recorded in the United Kingdom have been girls.

There is also no doubt that statement 2 has no such frequentist explanation (and hence must be subjective) since there is only one FA Cup next year, and we cannot somehow play the tournament many times in the same year and count the number of occasions on which the Spurs win.

But if we dig a little deeper here, things get rather murky. The $50.9 \%$ figure in statement 1 is actually based on many years of data that may disguise crucial trend information.

Suppose we discover that the percentage of girls born is increasing; say a hundred years ago $48.5 \%$ of babies were girls compared with $51.2 \%$ last year. Then surely the probability of a randomly selected newborn being a girl now is higher than $50.9 \%$ (and higher than $51.2 \%$ if the figures have been steadily increasing). And what exactly do we mean by a “randomly” selected baby. Surely, what we are interested in are specific babies such as “the next baby born to Mrs. Roberts of 213 White Hart Lane, London N17.” In that case the frequency data may need to be adjusted to take account of specific factors relevant to Mrs. Roberts. Both the general trend adjustments and the case specific adjustments here clearly require the subjective judgment of relevant experts. But that means, according to the frequentists, that their own approach is no longer valid since, as we saw earlier:

• The measure cannot be validated
• Different experts will give different subjective measures

统计代写|贝叶斯分析代写Bayesian Analysis代考|The Basics of Probability

In discussing the difference between the frequentist and subjective approaches to measuring uncertainty, we were careful in Chapter 4 not to mention the word probability. That is because we want to define probability in such a way that it makes sense for whatever reasonable approach to measuring uncertainty we choose, be it frequentist, subjective, or even an approach that nobody has yet thought of. To do this in Section $5.2$ we describe some properties (called axioms) that any reasonable measure of uncertainty should satisfy; then we define probability as any measure that satisfies those properties. The nice thing about this way of defining probability is that not only does it avoid the problem of vagueness, but it also means that we can have more than one measure of probability. In particular, we will see that both the frequentist and subjective approaches satisfy the axioms, and hence both are valid ways of defining probability.

In Section $5.3$ we introduce the crucial notion of probability distributions. In Section $5.4$ we use the axioms to define the crucial issue of independence of events. An especially important probability distribution-the Binomial distribution-which is based on the idea of independent events, is described in Section 5.5. Finally in Section $5.6$ we will apply the lessons learned in the chapter to solve some of the problems we set in Chapter 2 and debunk a number of other probability fallacies.

统计代写|贝叶斯分析代写Bayesian Analysis代考|When Frequentist and Subjective Approaches Merge

1. 有一个50.9%在英国刚出生的婴儿是女孩的可能性。
2. 有一个5%马刺明年有机会赢得足总杯。
从表面上看，似乎毫无疑问，陈述 1 可以通过一个常客论点来解释：过去 100 年50.9%在英国记录的所有新生儿中都是女孩。

• 无法验证该措施
• 不同的专家会给出不同的主观衡量标准

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

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|Frequentist versus Subjective View of Uncertainty

When we consider statements about uncertain events like
The next toss on a coin will be a head.

• A hurricane will destroy the White House within the next 5 years.
what we really want to do is measure the uncertainty of such events. In other words we want to be able to make statements like
• There is a 1 in 2 (or equivalently $50 \%$ ) chance that the next toss on a coin will be a head.
• There is a 1 in 10 million (or equivalently $0.000001 \%$ ) chance that a hurricane will destroy the White House within the next 5 years.
Although these statements are superficially similar, there are fundamental differences between them, which come down to the nature of the experiments that give rise to these outcomes. Specifically, whether the following assumptions are reasonable:
• Assumption 1 (repeatability of experiment)-The experiment is repeatable many times under identical conditions.
• Assumption 2 (independence of experiments)-Assuming the experiment is repeatable then the outcome of one experiment does not influence the result of any subsequent experiment.

统计代写|贝叶斯分析代写Bayesian Analysis代考|What If You Toss 100 Consecutive Heads

Your job (as a risk expert) is not to calculate the chance of tossing a head. Rather, your job is to calculate the chance that the next toss of the coin is a head (just look back at the original problems posed at the start of this chapter). So, if you observe what is known to be a fair coin being tossed 100 times and each time the result is heads, what do you believe are the chances of the next coin being heads?

A frequentist, given the fair coin assumption, would insist the answer is still $50 \%$. This is because the frequentist, with these assumptions, does not actually require any coin tosses to take place in practice. To the frequentist, the fair coin assumption means that the chance is always $50 \%$ on each throw. In other words, in making a prediction the frequentist must ignore the actual data that has been seen. The 100 consecutive heads would simply be considered a freak coincidence, that is, no more or less likely than any other random sequence of heads and tails. But then, the frequentist must ignore, for example,

1. The possibility that a fair coin can be tossed in such a way that makes heads more likely
2. That the coin tossed was not actually the fair coin assumed
In fact, we will see that such assumptions are irrational given the type of actual data observed. Only the subjective approach coupled with Bayesian reasoning will work effectively in such cases.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Frequentist versus Subjective View of Uncertainty

• 飓风将在未来 5 年内摧毁白宫。
我们真正想做的是衡量此类事件的不确定性。换句话说，我们希望能够做出如下陈述
• 有 2 个中的 1 个（或等效的50%) 下一次掷硬币的机会是正面。
• 1000 万分之一（或同等0.000001%) 飓风将在未来 5 年内摧毁白宫的可能性。
尽管这些陈述表面上相似，但它们之间存在根本差异，这归结为产生这些结果的实验​​的性质。具体来说，以下假设是否合理：
• 假设 1（实验的可重复性）——实验在相同条件下可重复多次。
• 假设 2（实验的独立性）——假设实验是可重复的，那么一个实验的结果不会影响任何后续实验的结果。

统计代写|贝叶斯分析代写Bayesian Analysis代考|What If You Toss 100 Consecutive Heads

1. 可以以更容易出现正面的方式投掷公平硬币的可能性
2. 投掷的硬币实际上并不是假设的公平硬币
事实上，我们将看到，鉴于观察到的实际数据类型，这种假设是不合理的。在这种情况下，只有结合贝叶斯推理的主观方法才能有效地发挥作用。

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

统计代写|贝叶斯分析代写Bayesian Analysis代考|STAT4102

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|Correlation Coefficient and p-Values

The correlation coefficient is a number between $-1$ and 1 that determines whether two paired sets of data (such as those for height and intelligence of a group of people) are related. The closer to 1 the more “confident” we are of a positive linear correlation and the closer to-1 the more confident we are of a negative linear correlation (which happens when, for example, one set of numbers tends to decrease when the other set increases as you might expect if you plotted a person’s age against the number of toys they possess). When the correlation coefficient is close to zero there is little evidence of any relationship.

Confidence in a relationship is formally determined not just by the correlation coefficient but also by the number of pairs in your data. If there are very few pairs then the coefficient needs to be very close to 1 or $-1$ for it to be deemed “statistically significant,” but if there are many pairs then a coefficient closer to 0 can still be considered “highly significant.”

The standard method that statisticians use to measure the “significance” of their empirical analyses is the $p$-value. Suppose we are trying to determine if the relationship between height and intelligence of people is significant and have data consisting of various pairs of values (height, intelligence) for a set of people; then we start with the “null hypothesis,” which, in this case is the statement “height and intelligence of people are unrelated.” The $p$-value is a number between 0 and 1 representing the probability that the data we have arisen if the null hypothesis were true. In medical trials the null hypothesis is typically of the form that “the use of drug X to treat disease $\mathrm{Y}$ is no better than not using the drug.”

The calculation of the $p$-value is based on a number of assumptions that are beyond the scope of this discussion, but people who need $p$-values can simply look them up in standard statistical tables (they are also computed automatically in Excel when you run Excel’s regression tool). The tables (or Excel) will tell you, for example, that if there are 100 pairs of data whose correlation coefficient is $0.254$, then the $p$-value is $0.01$. This means that there is a 1 in 100 chance that we would have seen these observations if the variables were unrelated.
A low $p$-value (such as $0.01$ ) is taken as evidence that the null hypothesis can be “rejected.” Statisticians say that a $p$-value of $0.01$ is “highly significant” or say that “the data is significant at the $0.01$ level.”

A competent researcher investigating a hypothesized relationship will set a $p$-value in advance of the empirical study. Typically, values of either $0.01$ or $0.05$ are used. If the data from the study results in a $p$-value of less than that specified in advance, the researchers will claim that their study is significant and it enables them to reject the null hypothesis and conclude that a relationship really exists.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Spurious Correlations

Although the preceding examples illustrate the danger of reading too much into dubious correlations between variables, the relationships we saw there did not arise purely by chance. In each case some additional common factors helped explain the relationship.

But many studies, including unfortunately many taken seriously, result in claims of causal relationships that are almost certainly due to nothing other than pure chance.

Although nobody would seriously take measures to stop Americans drinking beer in order to reduce Japanese child mortality, barely a day goes by when some decision maker or another somewhere in the world takes just as irrational a decision based on correlations that turn out to be just as spurious.

For example, on the day we first happened to be drafting this section (16 March 2009) the media was buzzing with the story that working night shifts resulted in an increased risk of breast cancer. This followed a World Health Organization study and it triggered the Danish government to make compensation awards to breast cancer sufferers who had worked night shifts. It is impossible to state categorically whether this result really is an example of a purely spurious correlation. But it is actually very simple to demonstrate why and how you will inevitably find a completely spurious correlation in such a study-which you might then wrongly claim is a causal relationship-if you measure enough things.

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。