.png){kind=icon}
.png){kind=icon}
Return
Learning recursive probability trees from probabilistic potentials
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2012.06.026.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A Recursive Probability Tree (RPT) is a data structure for representing the potentials involved in Probabilistic Graphical Models (PGMs). This structure is developed with the aim of capturing some types of independencies that cannot be represented with previous structures. This capability leads to improvements in memory space and computation time during inference. This paper describes a learning algorithm for building RPTs from probability distributions. The experimental analysis shows the proper behavior of the algorithm: it produces RPTs encoding good approximations of the original probability distributions. (C) 2012 Elsevier Inc. All rights reserved.
Keywords:
Bayesian networks
Probability trees
Recursive probability trees
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K