About this Course
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다음 전문 분야의 3개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 23시간 필요


자막: 영어

귀하가 습득할 기술

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

다음 전문 분야의 3개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 23시간 필요


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 25분 필요

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).

2 videos (Total 25 min)
2개의 동영상
Overview: MAP Inference9m
완료하는 데 1시간 필요

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 videos (Total 56 min), 1 quiz
4개의 동영상
Complexity of Variable Elimination12m
Graph-Based Perspective on Variable Elimination15m
Finding Elimination Orderings11m
1개 연습문제
Variable Elimination18m
완료하는 데 18시간 필요

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.

9 videos (Total 150 min), 3 quizzes
9개의 동영상
Properties of Cluster Graphs15m
Properties of Belief Propagation9m
Clique Tree Algorithm - Correctness18m
Clique Tree Algorithm - Computation16m
Clique Trees and Independence15m
Clique Trees and VE16m
BP In Practice15m
Loopy BP and Message Decoding21m
2개 연습문제
Message Passing in Cluster Graphs10m
Clique Tree Algorithm10m
완료하는 데 1시간 필요

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.

5 videos (Total 74 min), 1 quiz
5개의 동영상
Finding a MAP Assignment3m
Tractable MAP Problems15m
Dual Decomposition - Intuition17m
Dual Decomposition - Algorithm16m
1개 연습문제
MAP Message Passing4m
완료하는 데 14시간 필요

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.

5 videos (Total 100 min), 3 quizzes
5개의 동영상
Markov Chain Monte Carlo14m
Using a Markov Chain15m
Gibbs Sampling19m
Metropolis Hastings Algorithm27m
2개 연습문제
Sampling Methods14m
Sampling Methods PA Quiz8m
완료하는 데 26분 필요

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 video (Total 20 min), 1 quiz
1개의 동영상
1개 연습문제
Inference in Temporal Models6m
54개의 리뷰Chevron Right


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Probabilistic Graphical Models 2: Inference의 최상위 리뷰

대학: LLMar 12th 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.

대학: YPMay 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.



Daphne Koller

School of Engineering

스탠퍼드 대학교 정보

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 전문 분야 정보

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....
Probabilistic Graphical Models

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • 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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