Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

Pedro Bonilla-Nadal, Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral and Ofelia Paula Retamero
Additional contact information
Pedro Bonilla-Nadal: Computer Science and Artificial Intelligent Department, University of Granada, 18071 Granada, Spain
Andrés Cano: Computer Science and Artificial Intelligent Department, University of Granada, 18071 Granada, Spain
Manuel Gómez-Olmedo: Computer Science and Artificial Intelligent Department, University of Granada, 18071 Granada, Spain
Serafín Moral: Computer Science and Artificial Intelligent Department, University of Granada, 18071 Granada, Spain
Ofelia Paula Retamero: Computer Science and Artificial Intelligent Department, University of Granada, 18071 Granada, Spain

Mathematics, 2022, vol. 10, issue 14, 1-27

Abstract: The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For a long time now, a number of fields, such as medicine (and all healthcare-related areas) and education, have been particularly interested in obtaining relevant information from this stored data. This interest has resulted in the need to deal with increasingly complex problems which involve many different variables with a high degree of interdependency. This produces models (and in our case probabilistic graphical models) that are difficult to handle and that require very efficient techniques to store and use the information that quantifies the relationships between the problem variables. It has therefore been necessary to develop efficient structures, such as probability trees or value-based potentials, to represent the information. Even so, there are problems that must be treated using approximation since this is the only way that results can be obtained, despite the corresponding loss of information. The aim of this article is to show how the approximation can be performed with value-based potentials. Our experimental work is based on checking the behavior of this approximation technique on several Bayesian networks related to medical problems, and our experiments show that in some cases there are notable savings in memory space with limited information loss.

Keywords: probabilistic graphical models; bayesian networks; value-based potentials; approximate inference; medical applications (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/14/2542/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/14/2542/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:14:p:2542-:d:868195

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().