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
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Programa especializado: modelos gráficos de probabilidades Universidad de StanfordAcerca de este Curso
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Aprox. 38 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
- Inference
- Gibbs Sampling
- Markov Chain Monte Carlo (MCMC)
- Belief Propagation
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 3 en
Nivel avanzado
Aprox. 38 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Stanford
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.
Programa - Qué aprenderás en este curso
25 minutos para completar
Inference Overview
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).
25 minutos para completar
2 videos (Total 25 minutos)
1 hora para completar
Variable Elimination
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.
1 hora para completar
4 videos (Total 56 minutos)
18 horas para completar
Belief Propagation Algorithms
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.
18 horas para completar
9 videos (Total 150 minutos)
2 horas para completar
MAP Algorithms
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.
2 horas para completar
5 videos (Total 74 minutos)
15 horas para completar
Sampling Methods
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.
15 horas para completar
5 videos (Total 100 minutos)
1 hora para completar
Inference in Temporal Models
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.
1 hora para completar
1 video (Total 20 minutos)
Reseñas
- 5 stars71,09Â %
- 4 stars21,30Â %
- 3 stars5,27Â %
- 2 stars1,05Â %
- 1 star1,26Â %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
por JL8 de abr. de 2018
I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!
por HL18 de may. de 2020
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
por LL11 de mar. de 2017
Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.
por RL23 de feb. de 2021
Awesome class to gain better understanding of inference for graphical model
Acerca de Programa especializado: modelos gráficos de probabilidades
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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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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