Stanford University
Probabilistic Graphical Models 2: Inference
Stanford University

Probabilistic Graphical Models 2: Inference

Daphne Koller

Instructor: Daphne Koller

26,065 already enrolled

Gain insight into a topic and learn the fundamentals.
4.6

(485 reviews)

Advanced level
Designed for those already in the industry
38 hours to complete
3 weeks at 12 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

(485 reviews)

Advanced level
Designed for those already in the industry
38 hours to complete
3 weeks at 12 hours a week
Flexible schedule
Learn at your own pace

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Assessments

8 assignments

Taught in English

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This course is part of the Probabilistic Graphical Models Specialization
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There are 7 modules in this course

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).

What's included

2 videos

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.

What's included

4 videos1 assignment

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.

What's included

9 videos2 assignments1 programming assignment

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.

What's included

5 videos1 assignment

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.

What's included

5 videos2 assignments1 programming assignment

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.

What's included

1 video1 assignment

This module summarizes some of the topics that we covered in this course and discusses tradeoffs between different algorithms. It also includes the course final exam.

What's included

1 video1 assignment

Instructor

Instructor ratings
4.7 (19 ratings)
Daphne Koller
Stanford University
3 Courses95,743 learners

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4.6

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