About this Course
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Flexible deadlines

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Advanced Level

Approx. 23 hours to complete


Subtitles: English

Skills you will gain

InferenceGibbs SamplingMarkov Chain Monte Carlo (MCMC)Belief Propagation
Learners taking this Course are
  • Machine Learning Engineers
  • Data Scientists
  • Researchers
  • Research Assistants
  • Scientists

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 23 hours to complete


Subtitles: English

Syllabus - What you will learn from this course

25 minutes to complete

Inference Overview

2 videos (Total 25 min)
2 videos
Overview: MAP Inference9m
1 hour to complete

Variable Elimination

4 videos (Total 56 min), 1 quiz
4 videos
Complexity of Variable Elimination12m
Graph-Based Perspective on Variable Elimination15m
Finding Elimination Orderings11m
1 practice exercise
Variable Elimination18m
18 hours to complete

Belief Propagation Algorithms

9 videos (Total 150 min), 3 quizzes
9 videos
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
2 practice exercises
Message Passing in Cluster Graphs10m
Clique Tree Algorithm10m
1 hour to complete

MAP Algorithms

5 videos (Total 74 min), 1 quiz
5 videos
Finding a MAP Assignment3m
Tractable MAP Problems15m
Dual Decomposition - Intuition17m
Dual Decomposition - Algorithm16m
1 practice exercise
MAP Message Passing4m
14 hours to complete

Sampling Methods

5 videos (Total 100 min), 3 quizzes
5 videos
Markov Chain Monte Carlo14m
Using a Markov Chain15m
Gibbs Sampling19m
Metropolis Hastings Algorithm27m
2 practice exercises
Sampling Methods14m
Sampling Methods PA Quiz8m
26 minutes to complete

Inference in Temporal Models

1 video (Total 20 min), 1 quiz
1 practice exercise
Inference in Temporal Models6m
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Top reviews from Probabilistic Graphical Models 2: Inference

By ATAug 23rd 2019

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.

By ALAug 20th 2019

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.



Daphne Koller

School of Engineering

About Stanford University

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....

About the Probabilistic Graphical Models Specialization

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

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • 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

More questions? Visit the Learner Help Center.