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# 网课代修|概率模型代写Statistical Model代写|Statistics and Likelihood-Based Estimation

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## 网课代修|概率模型代写Statistical Model代写|Introduction

In the previous chapter we provided an overview of various $\mathrm{R}$ programming tools that will be needed when we start developing methods of model estimation. We also introduced the foremost probability and cumulative density functions that will be used in maximum likelihood estimation and simulation. Statistical modelling rests upon underlying probability functions, or mixtures of them, which are conceived to describe particular data situations. In this chapter we shall describe the relationship of data to probability and to likelihood, and show how these are in turn related to fitting and interpreting statistical models.

## 网课代修|概率模型代写Statistical Model代写|Statistical Models

Statistical models are used to describe a sample of data taken from a real or theoretical population. Statistical models can be described using one or more underlying probability distributions. The parameters of the distributions are estimated from the data, and may provide the basis for predicting additional data with the same distributional characteristics of the data being modeled. Models that can be defined in terms of a probability distribution having estimable parameters are called parametric models. We will focus our attention in this text on this type of model.

We note that non-parametric and semi- or partial-parametric models, as well as exact statistical models, have been constructed to deal with data that cannot be handled using normal parametric techniques. One set of nonparametric models is entirely, or in part, parameterized by smoothing splines. Statisticians generally turn to non-parametric model design when they have difficulty linearizing a continuous explanatory predictor so that it maintains an additive effect in an otherwise parametric model. Exact statistical models are used for dealing with sparse and unbalanced data. However, they demand huge amounts of computing power, and are thus limited in scope at this time.

## 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。