本课程是 Probabilistic Graphical Models 专项课程 专项课程的一部分

提供方

Probabilistic Graphical Models 专项课程

Stanford University

课程信息

4.6

286 个评分

•

45 个审阅

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....

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建议：7 hours/week...

字幕：English...

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

立即开始，按照自己的计划学习。

根据您的日程表重置截止日期。

建议：7 hours/week...

字幕：English...

Week

1This 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）

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

Complexity of Variable Elimination12分钟

Graph-Based Perspective on Variable Elimination15分钟

Finding Elimination Orderings11分钟

Variable Elimination18分钟

Week

2This 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

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

Message Passing in Cluster Graphs10分钟

Clique Tree Algorithm10分钟

Week

3This 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

Finding a MAP Assignment3分钟

Tractable MAP Problems15分钟

Dual Decomposition - Intuition17分钟

Dual Decomposition - Algorithm16分钟

MAP Message Passing4分钟

Week

4In 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

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

Sampling Methods14分钟

Sampling Methods PA Quiz8分钟

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

Inference in Temporal Models6分钟

4.6

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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!

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....

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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