Results 11 to 20 of about 39,041 (304)

Relational Dynamic Bayesian Networks

Journal of Artificial Intelligence Research, 2005
Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult.
Pedro M. Domingos, Sumit K. Sanghai, Daniel S. Weld   +2 more
openaire   +5 more sources

Characterization of Dynamic Bayesian Network-The Dynamic Bayesian Network as temporal network [PDF]

International Journal of Advanced Computer Science and Applications, 2011
في هذا التقرير، سنكون مهتمين بشبكة بايزي الديناميكية (DBNs) كنموذج يحاول دمج البعد الزمني مع عدم اليقين. نبدأ بأساسيات شبكة بايزي الديناميكية حيث نركز بشكل خاص على مفاهيم وخوارزميات الاستدلال والتعلم. ثم سنقدم مستويات وطرقًا مختلفة لإنشاء شبكات بايزي الديناميكية بالإضافة إلى مناهج دمج البعد الزمني في شبكة بايزي الثابتة.
Nabil Ghanmi, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara   +2 more
openaire   +1 more source

Dynamic networks from hierarchical bayesian graph clustering. [PDF]

PLoS ONE, 2010
Biological networks change dynamically as protein components are synthesized and degraded. Understanding the time-dependence and, in a multicellular organism, tissue-dependence of a network leads to insight beyond a view that collapses time-varying ...
Yongjin Park, Cristopher Moore, Joel S Bader   +2 more
doaj   +1 more source

Bayesian Spillover Graphs for Dynamic Networks

CoRR, 2022
We present Bayesian Spillover Graphs (BSG), a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects in a dynamic system. BSG leverages both an interpretable framework via forecast error variance decompositions (FEVD) and comprehensive uncertainty quantification via ...
Grace Deng, David S. Matteson
openaire   +3 more sources

Dynamic Bayesian networks for meeting structuring [PDF]

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
The paper is about the automatic structuring of multiparty meetings using audio information. We have used a corpus of 53 meetings, recorded using a microphone array and lapel microphones for each participant. The task was to segment meetings into a sequence of meeting actions, or phases.
Alfred Dielmann, Steve Renals
openaire   +3 more sources

Assessment of Dynamic Bayesian Models for Gas Turbine Diagnostics, Part 1: Prior Probability Analysis

Machines, 2021
The reliability and cost-effectiveness of energy conversion in gas turbine systems are strongly dependent on an accurate diagnosis of possible process and sensor anomalies.
Valentina Zaccaria, Amare Desalegn Fentaye, Konstantinos Kyprianidis   +2 more
doaj   +1 more source

Bayesian Nonparametrics for Sparse Dynamic Networks

, 2023
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution
Cian Naik, François Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla   +4 more
openaire   +3 more sources

The engineering skills training process modeling using dynamic bayesian nets

Радіоелектронні і комп'ютерні системи, 2021
The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks ...
Andrey Chukhray, Olena Havrylenko
doaj   +1 more source

Modeling dynamic reliability using dynamic Bayesian networks [PDF]

, 2006
This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this
Noyes, Daniel, Tchangani, Ayeley
core   +1 more source

Probabilistic Prognosis with Dynamic Bayesian Networks

International Journal of Prognostics and Health Management, 2015
This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats
Gregory Bartram, Sankaran Mahadevan
doaj   +1 more source