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# 计算机代写|图形模型代考Graphical Models代写|ECSE4810 Representation, Inference and Learning

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## 计算机代写|图形模型代考Graphical Models代写|Representation, Inference and Learning

There are three main aspects for each class of probabilistic graphical model, representation, inference and learning.

The representation is the basic property of each model, and it defines which entities constitute it and how these are related. For instance, all PGMs can be represented as graphs that define the structure of the model and by local functions that describe its parameters. However, the type of graph and the local functions vary for the different types of models.

Inference consists in answering different probabilistic queries based on the model and some evidence. For instance, obtaining the posterior probability distribution of a variable or set of variables given that other variables in the model are known. The challenge is how to do this efficiently.

To construct these models there are basically two alternatives: to build it “by hand” with the aid of domain experts or to induce the model from data. The emphasis in recent years has been to induce the models based on machine learning techniques, because it is difficult and costly to do it with the aid of experts. In particular obtaining the parameters for the models is usually done based on data, as humans tend to be bad estimators of probabilities.

An important property of these techniques from an application point of view is that they tend to separate the inference and learning techniques from the model. That is, as in other artificial intelligence representations such as logic and production rules, the reasoning mechanisms are general and can be applied to different models. As a result, the techniques developed for probabilistic inference and learning in each class of PGM, can be applied directly for different models in a variety of applications.

## 计算机代写|图形模型代考Graphical Models代写|Applications

Most real-world problems imply dealing with uncertainty and usually involve a large number of factors or variables to be considered when solving them. Probabilistic graphical models constitute an ideal framework to solve complex problems with uncertainty, so they are applied in a wide range of domains such as:

Medical diagnosis and decision making.

Mobile robot localization, navigation and planning.

Diagnosis for complex industrial equipment such as turbines and power plants.

User modeling for adaptive interfaces and intelligent tutors.

Speech recognition and natural language processing.

Pollution modeling and prediction.

Reliability analysis of complex processes.

Modeling the evolution of viruses.

Object recognition in computer vision.Error correction in communications.

Information retrieval.

Gesture and activity recognition.

Energy markets.

Agricultural planning.

Different types of PGMs are more appropriate for different applications, as will be shown in the following chapters when we present application examples for each class of PGM.

# 图形模型代写

## 计算机代写|图形模型代考Graphical Models代写|Applications

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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。