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.
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概率图模型 专项课程斯坦福大学课程信息
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第 2 门课程（共 3 门）
高级
完成时间大约为38 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Inference
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
高级
完成时间大约为38 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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斯坦福大学
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
授课大纲 - 您将从这门课程中学到什么
完成时间为 25 分钟
Inference Overview
This module provides a high-level overview of the main types of inference tasks typically encountered in graphical models: conditional probability queries, and finding the most likely assignment (MAP inference).
完成时间为 25 分钟
2 个视频 （总计 25 分钟）
完成时间为 1 小时
Variable Elimination
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.
完成时间为 1 小时
4 个视频 （总计 56 分钟）
完成时间为 18 小时
Belief Propagation Algorithms
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.
完成时间为 18 小时
9 个视频 （总计 150 分钟）
完成时间为 2 小时
MAP Algorithms
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.
完成时间为 2 小时
5 个视频 （总计 74 分钟）
完成时间为 15 小时
Sampling Methods
In this module, we discuss a class of algorithms that uses random sampling to provide approximate answers to conditional probability queries. Most commonly used among these is the class of Markov Chain Monte Carlo (MCMC) algorithms, which includes the simple Gibbs sampling algorithm, as well as a family of methods known as Metropolis-Hastings.
完成时间为 15 小时
5 个视频 （总计 100 分钟）
完成时间为 1 小时
Inference in Temporal Models
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.
完成时间为 1 小时
1 个视频 （总计 20 分钟）
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- 2 stars1.05%
- 1 star1.26%
来自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的热门评论
由 GV 提供Nov 27, 2017
great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally
由 RG 提供May 15, 2020
Great course. The assignments are old and are not worth doing it. But the content is good for those who are interested in Probabilistic Graphical Models basics.
由 PK 提供Dec 6, 2020
Amazing course offering a technical as well as intuitional understanding of the principles of doing inference
由 AK 提供Nov 4, 2017
This course induces lateral thinking and deep reasoning.
关于 概率图模型 专项课程
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.
常见问题
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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