Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.0
Journals
Axioms
Special Issues
Graphical Models
share announcement

Graphical Models

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: closed (28 February 2024) | Viewed by 1304

Special Issue Editor

Prof. Dhafer Malouche

E-Mail Website
Guest Editor
Department of Mathematics, Statistics, and Physics, College of Arts and Sciences, Qatar University, Doha 2713, Qatar
Interests: graphical models; public health; research and development; well being; survey methodology; data quality; consumer preferences; genetics; gender diversity

Special Issue Information

Dear Colleagues, 

The aim of this Special Issue is to bring together cutting-edge graphical models research and its advancements. We seek to explore the various aspects of them, including their theoretical foundations, novel methodologies, and practical applications. With this collection of papers, we aim to foster a deeper understanding of the principles and techniques underlying graphical models, their computational aspects, and their applications in diverse domains, such as machine learning, data science, network analysis, and bioinformatics. We invite researchers to contribute original work, including theoretical investigations, algorithm development, case studies, and interdisciplinary applications, to this "Graphical Models" Special Issue. By showcasing a wide range of perspectives and approaches, this Special Issue will serve as a platform for knowledge exchange and collaboration. 

Prof. Dhafer Malouche
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • probabilistic graphical models
  • Bayesian networks
  • Markov random fields
  • inference algorithms for graphical models
  • learning graphical models
  • causal graphical models
  • graphical models in machine learning
  • sparse graphical models
  • graphical model selection

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 370 KiB  
Article
Describing Conditional Independence Statements Using Undirected Graphs
by Dhafer Malouche
Axioms 2023, 12(12), 1109; https://doi.org/10.3390/axioms12121109 - 8 Dec 2023
Viewed by 878
Abstract
This paper investigates the capability of undirected graphs (UGs) to represent a set of Conditional Independence (CI) statements derived from a given probability distribution of a random vector. While it is established that certain axioms can govern this set, providing sufficient conditions for [...] Read more.
This paper investigates the capability of undirected graphs (UGs) to represent a set of Conditional Independence (CI) statements derived from a given probability distribution of a random vector. While it is established that certain axioms can govern this set, providing sufficient conditions for UGs to capture specific CI statements, our focus is on covariance and concentration graphs. These remain the only known families of UGs capable of describing CI statements. We explore the issue of complete representation of CI statements through their corresponding covariance and concentration graphs. Two parameters are defined, one each from the covariance and concentration graphs, to determine the limitations concerning the cardinality of the conditioning subset that the graph can represent. We establish a relationship between these parameters and the cardinality of the separators in each graph, providing a straightforward computational method to evaluate them. In conclusion, we enhance the aforementioned procedure and introduce criteria to ascertain, without additional computations, whether the graphs can fully represent a given set of CI statements. We demonstrate that either the concentration or the covariance graph forms a cycle, and when considered in conjunction, they can represent the entire relation. These criteria also enable us, in specific cases, to deduce the covariance graph from the concentration graph and vice versa. Full article
(This article belongs to the Special Issue Graphical Models)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop