Fast factorisation of probabilistic potentials and its application to approximate inference in Bayesian networks
We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to c...
Main Authors: | Cano, Andrés, Gómez Olmedo, Manuel, Pérez-Ariza, Cora B., Salmerón Cerdán, Antonio |
---|---|
Format: | info:eu-repo/semantics/article |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10835/4885 https://doi.org/10.1142/S0218488512500110 |
Similar Items
-
New strategies for finding multiplicative decompositions of probability trees
by: Martínez, Irene, et al.
Published: (2017) -
Dynamic Importance Sampling in Bayesian Networks Based on Probability Trees
by: Moral, Serafín, et al.
Published: (2017) -
MAP inference in dynamic hybrid Bayesian networks
by: Ramos López, Darío, et al.
Published: (2017) -
Probabilistic Models with Deep Neural Networks
by: Masegosa Arredondo, Andrés Ramón, et al.
Published: (2021) -
Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs*
by: Nielsen, Jens D., et al.
Published: (2017)