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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InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
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고급 단계
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스탠퍼드 대학교
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.
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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).
완료하는 데 25분 필요
2개 동영상 (총 25분)
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.
완료하는 데 1시간 필요
4개 동영상 (총 56분)
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.
완료하는 데 18시간 필요
9개 동영상 (총 150분)
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.
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5개 동영상 (총 74분)
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.
완료하는 데 14시간 필요
5개 동영상 (총 100분)
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.
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1개 동영상 (총 20분)
1개의 동영상
1개 연습문제
Inference in Temporal Models6m
검토
PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE의 최상위 리뷰
AT 제공2019년 8월 22일
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.
AL 제공2019년 8월 19일
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.
LC 제공2018년 7월 31일
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
RG 제공2020년 5월 15일
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.
Probabilistic Graphical Models 특화 과정 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.
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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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