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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20%
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20%
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第 2 门课程（共 3 门）
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高级
完成时间大约为36 小时
英语（English）
字幕：英语（English）
您将获得的技能
InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
学生职业成果
50%
完成这些课程后已开始新的职业生涯
20%
通过此课程获得实实在在的工作福利
20%
加薪或升职
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）
可灵活调整截止日期
根据您的日程表重置截止日期。
高级
完成时间大约为36 小时
英语（English）
字幕：英语（English）
提供方
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.
教学大纲 - 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）
4 个视频
Complexity of Variable Elimination12分钟
Graph-Based Perspective on Variable Elimination15分钟
Finding Elimination Orderings11分钟
1 个练习
Variable Elimination18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）
9 个视频
Properties of Cluster Graphs15分钟
Properties of Belief Propagation9分钟
Clique Tree Algorithm - Correctness18分钟
Clique Tree Algorithm - Computation16分钟
Clique Trees and Independence15分钟
Clique Trees and VE16分钟
BP In Practice15分钟
Loopy BP and Message Decoding21分钟
2 个练习
Message Passing in Cluster Graphs10分钟
Clique Tree Algorithm10分钟
完成时间为 1 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 1 小时
5 个视频 （总计 74 分钟）
5 个视频
Finding a MAP Assignment3分钟
Tractable MAP Problems15分钟
Dual Decomposition - Intuition17分钟
Dual Decomposition - Algorithm16分钟
1 个练习
MAP Message Passing4分钟
完成时间为 14 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 14 小时
5 个视频 （总计 100 分钟）
5 个视频
Simple Sampling23分钟
Markov Chain Monte Carlo14分钟
Using a Markov Chain15分钟
Gibbs Sampling19分钟
Metropolis Hastings Algorithm27分钟
2 个练习
Sampling Methods14分钟
Sampling Methods PA Quiz8分钟
完成时间为 26 分钟
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 26 分钟
1 个视频 （总计 20 分钟）
1 个视频
1 个练习
Inference in Temporal Models6分钟
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来自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的热门评论
由 AT 提供Aug 22, 2019
Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.
由 AL 提供Aug 19, 2019
I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.
由 LC 提供Jul 31, 2018
Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well
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.
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.
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
Execute the basic steps of a variable elimination or message passing algorithm
Understand how properties of the graph structure influence the complexity of exact inference, and thereby estimate whether exact inference is likely to be feasible
Go through the basic steps of an MCMC algorithm, both Gibbs sampling and Metropolis Hastings
Understand how properties of the PGM influence the efficacy of sampling methods, and thereby estimate whether MCMC algorithms are likely to be effective
Design Metropolis Hastings proposal distributions that are more likely to give good results
Compute a MAP assignment by exact inference
Honors track learners will be able to implement message passing algorithms and MCMC algorithms, and apply them to a real world problem
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