@misc{10481/70378,
year = {2021},
month = {7},
url = {http://hdl.handle.net/10481/70378},
abstract = {When dealing with complex models (i.e., models with
many variables, a high degree of dependency between
variables, or many states per variable), the efficient representation
of quantitative information in probabilistic
graphical models (PGMs) is a challenging task. To address
this problem, this study introduces several new structures,
aptly named value‐based potentials (VBPs), which are
based exclusively on the values. VBPs leverage repeated
values to reduce memory requirements. In the present
paper, they are compared with some common structures,
like standard tables or unidimensional arrays, and probability
trees (PT). Like VBPs, PTs are designed to reduce
the memory space, but this is achieved only if value repetitions
correspond to context‐specific independence
patterns (i.e., repeated values are related to consecutive
indices or configurations). VBPs are devised to overcome
this limitation. The goal of this study is to analyze the
properties of VBPs. We provide a theoretical analysis of
VBPs and use them to encode the quantitative information
of a set of well‐known Bayesian networks, measuring
the access time to their content and the computational
time required to perform some inference tasks.},
organization = {Spanish Government	PID2019-106758GB-C31	
TIN2016-77902-C3-2-P},
organization = {European Commission},
publisher = {John Wiley & Sons},
keywords = {Bayesian networks},
keywords = {Inference algorithms},
keywords = {Influence diagrams},
keywords = {Probabilistic graphical models},
title = {Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models},
doi = {10.1002/int.22573},
author = {Gómez Olmedo, Manuel and Cabañas, Rafael and Cano Utrera, Andrés and Moral García, Serafín and Retamero Pascual, Ofelia Paula},
}