本课程是 Probabilistic Graphical Models 专项课程 专项课程的一部分
Probabilistic Graphical Models 2: Inference
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课程信息
4.6
286 个评分
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.
您将获得的技能
InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
教学大纲 - 您将从这门课程中学到什么
Week
1Inference 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).
2 个视频（共 25 分钟）
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.
4 个视频（共 56 分钟）, 1 个测验
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
Week
2Belief 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.
9 个视频（共 150 分钟）, 3 个测验
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
Week
3MAP 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.
5 个视频（共 74 分钟）, 1 个测验
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
Week
4Sampling 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.
5 个视频（共 100 分钟）, 3 个测验
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
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.
1 个视频（共 20 分钟）, 1 个测验
1 个视频
1 个练习
Inference in Temporal Models6分钟
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热门审阅
创建者 YP•May 29th 2017
I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.
创建者 JL•Apr 9th 2018
I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!
讲师
Daphne Koller
ProfessorSchool of Engineering
关于 Stanford University
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.
关于 Probabilistic Graphical Models 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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