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 專項課程斯坦福大学課程信息
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學生職業成果
50%
完成這些課程後已開始新的職業生涯
20%
通過此課程獲得實實在在的工作福利
20%
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第 2 門課程（共 3 門）
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高級
完成時間大約為36 小時
英語（English）
字幕：英語（English）
您將獲得的技能
InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
學生職業成果
50%
完成這些課程後已開始新的職業生涯
20%
通過此課程獲得實實在在的工作福利
20%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 3 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為36 小時
英語（English）
字幕：英語（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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 25 分鐘
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).
完成時間為 25 分鐘
2 個視頻 （總計 25 分鐘）
完成時間為 1 小時
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.
完成時間為 1 小時
4 個視頻 （總計 56 分鐘）
4 個視頻
Complexity of Variable Elimination12分鐘
Graph-Based Perspective on Variable Elimination15分鐘
Finding Elimination Orderings11分鐘
1 個練習
Variable Elimination18分鐘
完成時間為 18 小時
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.
完成時間為 18 小時
9 個視頻 （總計 150 分鐘）
9 個視頻
Properties of Cluster Graphs15分鐘
Properties of Belief Propagation9分鐘
Clique Tree Algorithm - Correctness18分鐘
Clique Tree Algorithm - Computation16分鐘
Clique Trees and Independence15分鐘
Clique Trees and VE16分鐘
BP In Practice15分鐘
Loopy BP and Message Decoding21分鐘
2 個練習
Message Passing in Cluster Graphs10分鐘
Clique Tree Algorithm10分鐘
完成時間為 1 小時
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.
完成時間為 1 小時
5 個視頻 （總計 74 分鐘）
5 個視頻
Finding a MAP Assignment3分鐘
Tractable MAP Problems15分鐘
Dual Decomposition - Intuition17分鐘
Dual Decomposition - Algorithm16分鐘
1 個練習
MAP Message Passing4分鐘
完成時間為 14 小時
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.
完成時間為 14 小時
5 個視頻 （總計 100 分鐘）
5 個視頻
Simple Sampling23分鐘
Markov Chain Monte Carlo14分鐘
Using a Markov Chain15分鐘
Gibbs Sampling19分鐘
Metropolis Hastings Algorithm27分鐘
2 個練習
Sampling Methods14分鐘
Sampling Methods PA Quiz8分鐘
完成時間為 26 分鐘
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.
完成時間為 26 分鐘
1 個視頻 （總計 20 分鐘）
1 個視頻
1 個練習
Inference in Temporal Models6分鐘
審閱
來自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的熱門評論
由 AT 提供2019年8月22日
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2019年8月19日
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2018年7月31日
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2020年5月15日
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.
關於 概率图模型 專項課程
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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