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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|贝叶斯分析代写Bayesian Analysis代考|When Highly Probable Events Are Assumed to be Highly

The sheer number of similarities between these crimes is such that we can disregard the probability that they are unrelated. There is no such thing as coincidence, certainly not on this kind of scale.”

In fact the prosecutor’s argument is fundamentally flawed. Let us suppose that the crimes all took place within a 3-year period in the midtown area of New York. It is known that there were several thousands of robberies that took place in that area during that period. Being the central theater district, the vast majority of these muggings happened during the hours of 7 and $11: 30 \mathrm{p} . \mathrm{m}$. when the streets were filled with theatergoers and other tourists. In every recorded case the attacker did not know the victim and in almost every case the attacker struck from behind and stole something. Within the downtown area almost everywhere that people congregate is within 100 meters of both a bus stop and a theater. It therefore follows that almost all of the thousands of robberies that took place downtown during that period have exactly the same set of similarities listed.

For simplicity (and to help the prosecution case) let’s give the prosecutor the benefit of the doubt and assume there were just 1000 such crimes. Nobody is claiming that the same person committed all of these crimes. What is the maximum proportion of those crimes that could have been committed by the same person? Let’s suppose there is a truly super-robber who committed as many as 200 of the crimes. Then, if we randomly select any one of the 1000 crimes, there is a $20 \%$ chance ( $0.2$ probability) that the super-robber was involved.

Now pick out any five of these crimes randomly. What is the probability that the super-robber committed them all? The answer, assuming the Binomial distribution (where $n=5$ and $p=0.2$ ), is
$$0.2^{5}=0.00032$$
In other words, here are five crimes that satisfy the exact similarities to those claimed by the prosecutor. But, contrary to the claim of near certainty that these were committed by the same person, the actual chance of them all being committed by the same person are about 1 in 300 . Hence, in the absence of any other evidence, the claim that the crimes are all related is essentially bogus.

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|When Mundane Events Really Are Quite Incredible

The flipside of the fallacy of underestimating the probability of an event that appears to be very unlikely is to overestimate the probability of an event that seems to be likely. In Chapter 4 , Box $4.4$, we explained, for example, why every ordering of a pack of cards was a truly incredible event. As another more concrete example, suppose a fair coin is tossed 10 consecutive times. Which of the following outcomes is most likely?

• HH T H T H T T H T
• T T T T T T T T
Most people believe that the first is more likely than the second. But this is a fallacy. They are equally likely. People are confusing randomness (the first outcome seems much more random than the second) with probability. The number of different outcomes for 10 tosses of a coin is, as shown in Appendix A, equal to $2^{10}$. That is 1024. So each particular outcome has probability $1 / 1024$. Since the two sequences are just two of these particular outcomes, each has the same probability $1 / 1024 .$

The appearance of randomness in the first sequence compared to the second (which is the reason why people think the first sequence is more likely) arises because it looks like lots of other possible sequences. Specifically, this sequence involves five heads and five tails, and there are many sequences involving five heads and five tails.

# 贝叶斯分析代考

0.25=0.00032

## 统计代写|贝叶斯分析代写Bayesian Analysis代考|When Mundane Events Really Are Quite Incredible

• 嗯嗯嗯嗯嗯嗯嗯嗯嗯嗯嗯嗯嗯嗯
• TTTTTTTT
大多数人认为第一个比第二个更有可能。但这是一个谬误。他们同样有可能。人们将随机性（第一个结果似乎比第二个结果随机得多）与概率相混淆。如附录 A 所示，投掷 10 次硬币的不同结果的数量等于210. 那是 1024。所以每个特定的结果都有概率1/1024. 由于这两个序列只是这些特定结果中的两个，因此每个序列具有相同的概率1/1024.

## 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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