Variable elimination (VE) is a simple and general exact inference algorithm in probabilistic graphical models, such as Bayesian networks and Markov random fields. It can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables. The algorithm has exponential time complexity, but could be efficient in practice for low-treewidth graphs, if the proper elimination order is used.
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| - Algoritmo de eliminación de variables (es)
- Variable elimination (en)
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| - Variable elimination (VE) is a simple and general exact inference algorithm in probabilistic graphical models, such as Bayesian networks and Markov random fields. It can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables. The algorithm has exponential time complexity, but could be efficient in practice for low-treewidth graphs, if the proper elimination order is used. (en)
- El algoritmo de eliminación de variables es un algoritmo de adquisición de conocimiento probabilístico a partir de una red bayesiana. Dada una red bayesiana y una serie de valores observados para ciertas variables, denominadas de evidencia, se obtiene las probabilidades esperadas de una variable de consulta. (es)
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| - El algoritmo de eliminación de variables es un algoritmo de adquisición de conocimiento probabilístico a partir de una red bayesiana. Dada una red bayesiana y una serie de valores observados para ciertas variables, denominadas de evidencia, se obtiene las probabilidades esperadas de una variable de consulta. El algoritmo trata de hacer uso de diversas técnicas para reducir los cálculos en la medida de lo posible. El nombre de eliminación de variables proviene de desechar del cálculo de la probabilidad de la variable de consulta a aquellas variables que no tienen ninguna relación de dependencia. (es)
- Variable elimination (VE) is a simple and general exact inference algorithm in probabilistic graphical models, such as Bayesian networks and Markov random fields. It can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables. The algorithm has exponential time complexity, but could be efficient in practice for low-treewidth graphs, if the proper elimination order is used. (en)
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