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# 统计代写|贝叶斯统计代写beyesian statistics代考|Starting with Probability

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## 统计代写|贝叶斯统计代写beyesian statistics代考|Definition of Probability

Statistical processes and measurements are usually uncertain because the outcomes cannot be precisely predicted. In the process of collecting statistical data, we generally assume that there is a true value, which lies within the measurements we make. This means there is uncertainty in collecting the data. Generally, our goal is to find the best estimate of our measured data under uncertainty.

To measure the uncertainty of statistical processes and events, we resort to the probability theory that is often used to describe uncertain quantities. We explain the concept of probability in the subsequent sections of this chapter.

In numerous situations, we have to describe an event in terms of its chances of occurrence. For example, weather forecasts tell us about the chances of rain on any particular day. In sports analysis, the probability of a team winning is given before or during the actual game.

## 统计代写|贝叶斯统计代写beyesian statistics代考|Some Important Definitions

In statistical studies or experiments, the quantities to be measured are named random variables. An observation is a specific outcome of the experiment. Numerous observations collected from the study constitute the data, and an assortment of all possible outcomes of the experiment is called the population.

Practically, we cannot observe the whole population. As an alternate, we take a sample, that is, a small portion of the population. We try to get the sample from the population in such a way that it should represent the whole population. Thus, we take a random sample in which all members of the population are equally likely to be included in the sample.

## 统计代写|贝叶斯统计代写BEYESIAN STATISTICS代考|Samples Spaces and Events

The outcomes of statistical experiments are generally random. The probability theory is employed to study the random behavior of statistical experiments. The set of all possible outcomes of an experiment forms the sample space specific to the experiment. We usually denote the sample space by $S$. An element that is an outcome of $S$ is represented by $s$. The sample space is selected to get one outcome from one run of the experiment. The sample space can be finite or infinite.
For example, if we roll a dice, we get one of the numbers from 1 to 6 . The sample space of this experiment is given as,
$$S={1,2,3,4,5,6} .$$
As another example, tossing a coin twice will constitute the following sample space:
$$\mathrm{S}={\mathrm{HH}, \mathrm{HT}, \mathrm{TH}, \mathrm{TT}} .$$

## 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。