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## 20%

### 您将获得的技能

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation

1

## Inference Overview

2 个视频 （总计 25 分钟）
2 个视频
Overview: MAP Inference9分钟

## Variable Elimination

4 个视频 （总计 56 分钟）
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
2

## Belief Propagation Algorithms

9 个视频 （总计 150 分钟）
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
3

## MAP Algorithms

5 个视频 （总计 74 分钟）
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
4

## Sampling Methods

5 个视频 （总计 100 分钟）
5 个视频
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

1 个视频 （总计 20 分钟）
1 个视频
1 个练习
Inference in Temporal Models6分钟
4.6
60 条评论

### 来自Probabilistic Graphical Models 2: Inference的热门评论

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.

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.

### 关于 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....

## 关于 概率图模型 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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• 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