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.
Este curso forma parte de Programa especializado: modelos gráficos de probabilidades
Ofrecido Por
Acerca de este Curso
Habilidades que obtendrás
- Inference
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Ofrecido por
Programa - Qué aprenderás en este curso
Inference Overview
Variable Elimination
Belief Propagation Algorithms
MAP Algorithms
Sampling Methods
Inference in Temporal Models
Reseñas
- 5 stars71,33 %
- 4 stars21,12 %
- 3 stars5,23 %
- 2 stars1,04 %
- 1 star1,25 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
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.
great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally
Very great course! A lot of things have been learnt. The lectures, quiz and assignments clear up all key concepts. Especially, assignments are wonderful!
Great course! Course has filled gaps in my knowledge from statistics and similar sciences.
Acerca de Programa especializado: modelos gráficos de probabilidades

Preguntas Frecuentes
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¿Hay ayuda económica disponible?
Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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