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.
This course is part of the Probabilistic Graphical Models Specialization
About this Course
Skills you will gain
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Syllabus - What you will learn from this course
Belief Propagation Algorithms
Inference in Temporal Models
- 5 stars71.18%
- 4 stars21.08%
- 3 stars5.21%
- 2 stars1.25%
- 1 star1.25%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
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
Awesome class to gain better understanding of inference for graphical model
Great introduction to inference. Requires some extra reading from the textbook.
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.
About the Probabilistic Graphical Models Specialization
Frequently Asked Questions
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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