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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/probabilistic-graphical-models-2-inference
课程评论：没有评论
课程名称：概率图模型2：推理 概述：概率图模型（PGMs）为编码复杂领域中的概率分布提供了丰富的框架。这些表示位于统计学和计算机科学的交叉点，依赖于概率论、图算法和机器学习等概念。PGMs在许多应用中（如医学诊断、图像理解、语音识别和自然语言处理等）是先进方法的基础，同时也是许多机器学习问题的基本工具。本课程是该系列的第二部分，重点讨论概率推理，即如何利用PGM回答问题。尽管PGM通常描述的是高维分布，但其结构经过设计，能够高效地回答问题。课程介绍了不同类型推理任务的精确和近似算法，并讨论了各自最适合的应用场景。荣誉追踪部分推荐包含两个动手编程作业，学员将在其中实现和应用最常用的精确和近似算法的关键例程，以解决真实世界的问题。 课程大纲： 1. **变量消元**：介绍图模型中用于精确推理的最简单算法——变量消元，描述该算法并分析其在图结构属性上的复杂性。 2. **信念传播算法**：描述图模型中精确推理的另一种视角：通过消息传递在编码一个子集变量因子聚类之间进行的推理框架，专注于基本框架及其在团树传播的精确实例化。 3. **MAP算法**：描述用于查找概率图模型中最可能分配的算法（MAP推理），探讨消息传递算法的细节及其与计算条件概率的相似性，并讨论解码结果以构造单一分配的方法。 4. **动态模型中的推理**：简要讨论将之前学习的精确或近似推理算法应用于动态贝叶斯网络的复杂性。 5. **推理总结**：总结课程中覆盖的一些主题，并讨论不同算法之间的权衡，同时包含课程的期末考试。
Part: 1
Title:Variable Elimination
Description:This module presents the simplest algorithm for exact inference in graphical models: variable elimination. We describe the algorithm, and analyze its complexity in terms of properties of the graph structure.
Part: 2
Title:Belief Propagation Algorithms
Description:This module describes an alternative view of exact inference in graphical models: that of message passing between clusters each of which encodes a factor over a subset of variables. This framework provides a basis for a variety of exact and approximate inference algorithms. We focus here on the basic framework and on its instantiation in the exact case of clique tree propagation. An optional lesson describes the loopy belief propagation (LBP) algorithm and its properties.
Part: 3
Title:MAP Algorithms
Description:This module describes algorithms for finding the most likely assignment for a distribution encoded as a PGM (a task known as MAP inference). We describe message passing algorithms, which are very similar to the algorithms for computing conditional probabilities, except that we need to also consider how to decode the results to construct a single assignment. In an optional module, we describe a few other algorithms that are able to use very different techniques by exploiting the combinatorial optimization nature of the MAP task.
Part: 4
Title:Inference in Temporal Models
Description:In this brief lesson, we discuss some of the complexities of applying some of the exact or approximate inference algorithms that we learned earlier in this course to dynamic Bayesian networks.
Part: 5
Title:Inference Summary
Description:This module summarizes some of the topics that we covered in this course and discusses tradeoffs between different algorithms. It also includes the course final exam.
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.
