### 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 But that means, according to the frequentists, that their own approach is no longer valid since, as we saw earlier: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

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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。