A Petri Net-Based Tool for the Analysis of Generalized Continuous Time Bayesian Networks

2013 ◽  
pp. 118-143 ◽  
Author(s):  
Daniele Codetta-Raiteri ◽  
Luigi Portinale
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Petri Net ◽  
Continuous Time ◽  
Dynamic Bayesian Network ◽  
Software Tool ◽  
Inference Algorithms ◽  
Continuous Time Bayesian Networks ◽  
Generalized Stochastic Petri Net

A software tool for the analysis of Generalized Continuous Time Bayesian Networks (GCTBN) is presented. GCTGBN extend CTBN introducing in addition to continuous time-delayed variables, non-delayed or “immediate” variables. The tool is based on the conversion of a GCTBN model into a Generalized Stochastic Petri Net (GSPN), which is an actual mean to perform the inference (analysis) of the GCTBN. Both the inference tasks (prediction and smoothing) can be performed in this way. The architecture and the methodologies of the tool are presented. In particular, the conversion rules from GCTBN to GSPN are described, and the inference algorithms exploiting GSPN transient analysis are presented. A running example supports their description: a case study is modelled as a GCTBN and analyzed by means of the tool. The results are verified by modelling and analyzing the system as a Dynamic Bayesian Network, another form of Bayesian Network, assuming discrete time.

scholarly journals Learning Continuous Time Bayesian Networks in Non-stationary Domains

2018 ◽  
Author(s):  
Simone Villa ◽  
Fabio Stella
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Real World ◽  
Continuous Time ◽  
Real World Data ◽  
Change Over Time ◽  
World Data ◽  
Continuous Time Bayesian Networks ◽  
Continuous Time Bayesian Network ◽  
Over Time

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

scholarly journals A dynamic Bayesian network approach for prognosis computations on discrete state systems

2017 ◽  
Vol 231 (5) ◽  
pp. 516-533 ◽  
Author(s):  
Josquin Foulliaron ◽  
Laurent Bouillaut ◽  
Patrice Aknin ◽  
Anne Barros
Keyword(s):  
Complex Systems ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Multicomponent System ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Discrete State ◽  
Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

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. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

scholarly journals Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism

2020 ◽  
Vol 6 (3) ◽  
pp. 108
Author(s):  
Enzo Acerbi ◽  
Marcela Hortova-Kohoutkova ◽  
Tsokyi Choera ◽  
Nancy Keller ◽  
Jan Fric ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Continuous Time ◽  
Regulatory Networks ◽  
Time Course ◽  
Metabolic Networks ◽  
De Novo ◽  
Tryptophan Metabolism ◽  
Reconstruction Method ◽  
Fungal Virulence ◽  
Continuous Time Bayesian Networks

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)—belonging in the heme dioxygenase family—degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+). As such, they are expressed by a variety of species, including fungi. Interestingly, Aspergillus may degrade l-tryptophan not only via IDO but also via alternative pathways. Deciphering the molecular interactions regulating tryptophan metabolism is particularly critical for novel drug target discovery designed to control pathogen determinants in invasive infections. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling l-tryptophan metabolism. The method unravels a possible novel approach to target fungal virulence factors during infection. Furthermore, this study represents the first application of continuous-time Bayesian networks as a gene network reconstruction method in Aspergillus metabolism. The experiment showed that the applied computational approach may improve the understanding of metabolic networks over traditional pathways.

Sign in / Sign up

Export Citation Format

Share Document