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Duality of Graphical Models and Tensor Networks [PDF]

open access: yesarXiv, 2017
In this article we show the duality between tensor networks and undirected graphical models with discrete variables. We study tensor networks on hypergraphs, which we call tensor hypernetworks. We show that the tensor hypernetwork on a hypergraph exactly corresponds to the graphical model given by the dual hypergraph. We translate various notions under
Robeva, Elina, Seigal, Anna
arxiv   +3 more sources

Comparison of simple graphical process models [PDF]

open access: yesJournal of Information and Organizational Sciences, 2012
Comparing structure of graphical process models can reveal different variations of processes. Since most contemporary norms for process modeling lean on directed connectivity of objects in the model, their connections structure form sequences which can ...
Katarina Tomicic-Pupek   +2 more
doaj   +4 more sources

Fast and Privacy-Preserving Federated Joint Estimator of Multi-sUGMs

open access: yesIEEE Access, 2021
Learning multiple related graphs from many distributed and privacy-required resources is an important and common task in neuroscience applications. Medical researchers can comprehensively investigate the diagnostic evidence and understand the cause of ...
Xiao Tan, Tianyi Ma, Tongtong Su
doaj   +1 more source

Community Detection in Social Networks Considering Social Behaviors

open access: yesIEEE Access, 2022
The study of community detection in networks has drawn great attention in recent years. To find communities and to understand community semantics, both network topology and network content are utilized. Unfortunately, none of them can explain the driving
Yingkui Wang   +4 more
doaj   +1 more source

Undirected Structural Markov Property for Bayesian Model Determination

open access: yesMathematics, 2023
This paper generalizes the structural Markov properties for undirected decomposable graphs to arbitrary ones. This helps us to exploit the conditional independence properties of joint prior laws to analyze and compare multiple graphical structures, while
Xiong Kang, Yingying Hu, Yi Sun
doaj   +1 more source

A Comparative Study on the Skill of CMIP6 Models to Preserve Daily Spatial Patterns of Monsoon Rainfall Over India

open access: yesFrontiers in Climate, 2021
South Asian monsoon is a phenomena that plays out during June-September every year, due to the northward shift of the ITCZ which causes heavy rainfall over many countries of South Asia, including India.
Adway Mitra
doaj   +1 more source

Classification of LiDAR Point Clouds Using Supervoxel-Based Detrended Feature and Perception-Weighted Graphical Model

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Interpretation of 3-D scene through LiDAR point clouds has been a hot research topic for decades. To utilize measured points in the scene, assigning unique tags to the points of the scene with labels linking to individual objects plays a crucial role in ...
Yusheng Xu   +6 more
doaj   +1 more source

Modeling Categorical Variables by Mutual Information Decomposition

open access: yesEntropy, 2023
This paper proposed the use of mutual information (MI) decomposition as a novel approach to identifying indispensable variables and their interactions for contingency table analysis.
Jiun-Wei Liou   +2 more
doaj   +1 more source

Conditional Random Field-Guided Multi-Focus Image Fusion

open access: yesJournal of Imaging, 2022
Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important.
Odysseas Bouzos   +2 more
doaj   +1 more source

Graphical Notation for Document Database Modeling

open access: yesОткрытое образование (Москва), 2021
Goals and objectives. Graphical models have proven to be a reliable, clear and convenient tool for creating sketch models of databases. Most of the existing notations are designed for the relational data model, the dominant data model for the last thirty
M. V. Smirnov, R. S. Tolmasov
doaj   +1 more source

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