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Journal articles on the topic 'Continuous Time Bayesian Networks'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Bhattacharjya, Debarun, Karthikeyan Shanmugam, Tian Gao, Nicholas Mattei, Kush Varshney, and Dharmashankar Subramanian. "Event-Driven Continuous Time Bayesian Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3259–66. http://dx.doi.org/10.1609/aaai.v34i04.5725.

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We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We d

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Villa, Simone, and Fabio Stella. "Learning Continuous Time Bayesian Networks in Non-stationary Domains." Journal of Artificial Intelligence Research 57 (September 20, 2016): 1–37. http://dx.doi.org/10.1613/jair.5126.

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Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary continuous time Bayesian networks to that of non-stationary dynamic Bayesian net

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Xu, J., and C. R. Shelton. "Intrusion Detection using Continuous Time Bayesian Networks." Journal of Artificial Intelligence Research 39 (December 23, 2010): 745–74. http://dx.doi.org/10.1613/jair.3050.

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Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed upda

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Perreault, Logan, Monica Thornton, John Sheppard, and Joseph DeBruycker. "Disjunctive interaction in continuous time Bayesian networks." International Journal of Approximate Reasoning 90 (November 2017): 253–71. http://dx.doi.org/10.1016/j.ijar.2017.07.011.

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5

Perreault, Logan, and John Sheppard. "Compact structures for continuous time Bayesian networks." International Journal of Approximate Reasoning 109 (June 2019): 19–41. http://dx.doi.org/10.1016/j.ijar.2019.03.005.

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6

Stella, F., and Y. Amer. "Continuous time Bayesian network classifiers." Journal of Biomedical Informatics 45, no. 6 (2012): 1108–19. http://dx.doi.org/10.1016/j.jbi.2012.07.002.

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Linzner, Dominik, and Heinz Koeppl. "Active learning of continuous-time Bayesian networks through interventions*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (2021): 124001. http://dx.doi.org/10.1088/1742-5468/ac3908.

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Abstract We consider the problem of learning structures and parameters of continuous-time Bayesian networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences. A popular approach to overcome this is Bayesian optimal experimental design (BOED). However, BOED becomes infeasible in high-dimensional settings, as it involves integration over all possible experimental outcomes. We propose a novel criterion for experimental design based on a variational approximatio

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Sturlaugson, Liessman, and John W. Sheppard. "Uncertain and negative evidence in continuous time Bayesian networks." International Journal of Approximate Reasoning 70 (March 2016): 99–122. http://dx.doi.org/10.1016/j.ijar.2015.12.013.

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9

Hosoda, Shion, Tsukasa Fukunaga, and Michiaki Hamada. "Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model." Bioinformatics 37, Supplement_1 (2021): i16—i24. http://dx.doi.org/10.1093/bioinformatics/btab287.

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Abstract Motivation Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results In this study, we developed unsupervised learning-based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying micro

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10

Park, Cheol Young, Kathryn Blackmond Laskey, Paulo C. G. Costa, and Shou Matsumoto. "Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks." Applied Sciences 9, no. 10 (2019): 2055. http://dx.doi.org/10.3390/app9102055.

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Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be perfo

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Codecasa, Daniele, and Fabio Stella. "Learning continuous time Bayesian network classifiers." International Journal of Approximate Reasoning 55, no. 8 (2014): 1728–46. http://dx.doi.org/10.1016/j.ijar.2014.05.005.

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12

Shelton, C. R., and G. Ciardo. "Tutorial on Structured Continuous-Time Markov Processes." Journal of Artificial Intelligence Research 51 (December 23, 2014): 725–78. http://dx.doi.org/10.1613/jair.4415.

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A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and

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Schupbach, Jordan, Elliott Pryor, Kyle Webster, and John Sheppard. "A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks." IEEE Instrumentation & Measurement Magazine 26, no. 5 (2023): 3–11. http://dx.doi.org/10.1109/mim.2023.10208251.

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14

Sturlaugson, Liessman, Logan Perreault, and John W. Sheppard. "Factored performance functions and decision making in continuous time Bayesian networks." Journal of Applied Logic 22 (July 2017): 28–45. http://dx.doi.org/10.1016/j.jal.2016.11.030.

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15

Codetta-Raiteri, Daniele. "Applying Generalized Continuous Time Bayesian Networks to a reliability case study." IFAC-PapersOnLine 48, no. 21 (2015): 676–81. http://dx.doi.org/10.1016/j.ifacol.2015.09.605.

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16

Codecasa, Daniele, and Fabio Stella. "Classification and clustering with continuous time Bayesian network models." Journal of Intelligent Information Systems 45, no. 2 (2014): 187–220. http://dx.doi.org/10.1007/s10844-014-0345-0.

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17

Wei, Xiaohan, Yulai Zhang, and Cheng Wang. "Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks." Entropy 24, no. 10 (2022): 1351. http://dx.doi.org/10.3390/e24101351.

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Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables a

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18

Acerbi, Enzo, Marcela Hortova-Kohoutkova, Tsokyi Choera, et al. "Modeling Approaches Reveal New Regulatory Networks in Aspergillus fumigatus Metabolism." Journal of Fungi 6, no. 3 (2020): 108. http://dx.doi.org/10.3390/jof6030108.

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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 part

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19

Bregoli, Alessandro, Marco Scutari, and Fabio Stella. "A constraint-based algorithm for the structural learning of continuous-time Bayesian networks." International Journal of Approximate Reasoning 138 (November 2021): 105–22. http://dx.doi.org/10.1016/j.ijar.2021.08.005.

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20

Li, Yan-Feng, Jinhua Mi, Yu Liu, Yuan-Jian Yang, and Hong-Zhong Huang. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229, no. 6 (2015): 530–41. http://dx.doi.org/10.1177/1748006x15588446.

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21

Sturlaugson, Liessman, and John W. Sheppard. "Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (2015): 346–69. http://dx.doi.org/10.1137/140953848.

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22

Devni, Prima Sar, Rosadi Dedi, Ronnie Effendie Adhitya, and Danardono. "K-means and bayesian networks to determine building damage levels." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 719–27. https://doi.org/10.12928/TELKOMNIKA.v17i2.11756.

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Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of

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23

Codetta-Raiteri, Daniele, and Luigi Portinale. "Generalized Continuous Time Bayesian Networks as a modelling and analysis formalism for dependable systems." Reliability Engineering & System Safety 167 (November 2017): 639–51. http://dx.doi.org/10.1016/j.ress.2017.04.014.

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24

Beaudry, Eric, Froduald Kabanza, and Francois Michaud. "Planning for Concurrent Action Executions Under Action Duration Uncertainty Using Dynamically Generated Bayesian Networks." Proceedings of the International Conference on Automated Planning and Scheduling 20 (May 25, 2021): 10–17. http://dx.doi.org/10.1609/icaps.v20i1.13400.

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An interesting class of planning domains, including planning for daily activities of Mars rovers, involves achievement of goals with time constraints and concurrent actions with probabilistic durations. Current probabilistic approaches, which rely on a discrete time model, introduce a blow up in the search state-space when the two factors of action concurrency and action duration uncertainty are combined. Simulation-based and sampling probabilistic planning approaches would cope with this state explosion by avoiding storing all the explored states in memory, but they remain approximate solutio

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25

Boudali, H., and J. B. Dugan. "A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework." IEEE Transactions on Reliability 55, no. 1 (2006): 86–97. http://dx.doi.org/10.1109/tr.2005.859228.

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26

Villa, S., and F. Stella. "A continuous time Bayesian network classifier for intraday FX prediction." Quantitative Finance 14, no. 12 (2014): 2079–92. http://dx.doi.org/10.1080/14697688.2014.906811.

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27

Gatti, E., D. Luciani, and F. Stella. "A continuous time Bayesian network model for cardiogenic heart failure." Flexible Services and Manufacturing Journal 24, no. 4 (2011): 496–515. http://dx.doi.org/10.1007/s10696-011-9131-2.

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28

Liu, Manxia, Fabio Stella, Arjen Hommersom, Peter J. F. Lucas, Lonneke Boer, and Erik Bischoff. "A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity." Artificial Intelligence in Medicine 95 (April 2019): 104–17. http://dx.doi.org/10.1016/j.artmed.2018.10.002.

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29

WU, CHUNG-HSIEN, JHING-FA WANG, CHAUG-CHING HUANG, and JAU-YIEN LEE. "SPEAKER-INDEPENDENT RECOGNITION OF ISOLATED WORDS USING CONCATENATED NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 05 (1991): 693–714. http://dx.doi.org/10.1142/s0218001491000417.

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A speaker-independent isolated word recognizer is proposed. It is obtained by concatenating a Bayesian neural network and a Hopfield time-alignment network. In this system, the Bayesian network outputs the a posteriori probability for each speech frame, and the Hopfield network is then concatenated for time warping. A proposed splitting Learning Vector Quantization (LVQ) algorithm derived from the LBG clustering algorithm and the Kohonen LVQ algorithm is first used to train the Bayesian network. The LVQ2 algorithm is subsequently adopted as a final refinement step. A continuous mixture of Gaus

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30

Bobrowski, Omer, Ron Meir, and Yonina C. Eldar. "Bayesian Filtering in Spiking Neural Networks: Noise, Adaptation, and Multisensory Integration." Neural Computation 21, no. 5 (2009): 1277–320. http://dx.doi.org/10.1162/neco.2008.01-08-692.

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A key requirement facing organisms acting in uncertain dynamic environments is the real-time estimation and prediction of environmental states, based on which effective actions can be selected. While it is becoming evident that organisms employ exact or approximate Bayesian statistical calculations for these purposes, it is far less clear how these putative computations are implemented by neural networks in a strictly dynamic setting. In this work, we make use of rigorous mathematical results from the theory of continuous time point process filtering and show how optimal real-time state estima

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Dui, Hongyan, Jiaying Song, and Yun-an Zhang. "Reliability and Service Life Analysis of Airbag Systems." Mathematics 11, no. 2 (2023): 434. http://dx.doi.org/10.3390/math11020434.

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Airbag systems are important to a car’s safety protection system. To further improve the reliability of the system, this paper analyzes the failure mechanism of automotive airbag systems and establishes a dynamic fault tree model. The dynamic fault tree model is transformed into a continuous-time Bayesian network by introducing a unit step function and an impulse function, from which the failure probability of the system is calculated. Finally, the system reliability and average life are calculated and analyzed and compared with the sequential binary decision diagram method. The results show t

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da Silva, Rafael Luiz, Boxuan Zhong, Yuhan Chen, and Edgar Lobaton. "Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks." Information 13, no. 7 (2022): 338. http://dx.doi.org/10.3390/info13070338.

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Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for joi

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WANG, Xiaoming. "Reliability Modeling and Evaluation for Rectifier Feedback System Based on Continuous Time Bayesian Networks Under Fuzzy Numbers." Journal of Mechanical Engineering 51, no. 14 (2015): 167. http://dx.doi.org/10.3901/jme.2015.14.167.

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34

Wei, Jiacheng, Tong Chen, Haolin Wen, and Haobang Liu. "Time-Varying Reliability Analysis of Integrated Power System Based on Dynamic Bayesian Network." Systems 13, no. 7 (2025): 541. https://doi.org/10.3390/systems13070541.

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In response to the limitations of traditional static reliability analysis methods in characterizing the reliability changes of the Integrated Power System, this paper proposes a time-varying reliability analysis framework based on a Dynamic Bayesian Network. By embedding a multi-physics coupled degradation model into the conditional probability nodes of the Dynamic Bayesian Network, a joint stochastic differential equation for the degradation process was constructed, and the dynamic correlation between continuous degradation and discrete fault events throughout the entire life cycle was achiev

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Sabet, M. Amin, and Behnam Ghavami. "Statistical soft error rate estimation of combinational circuits using Bayesian networks." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 5 (2016): 1760–73. http://dx.doi.org/10.1108/compel-09-2015-0317.

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Purpose With continuous scaling of digital circuit CMOS technology, the vulnerability of these circuits are significantly increasing against the soft errors. On the other hand, the effects of process variation in the electrical properties of nano-scale circuits, have introduced the statistical methods as an unavoidable choice for the soft error rate (SER) estimation. The purpose of this paper is to provide a statistical soft error rate (SSER) estimation approach for combinational circuits in the presence of process variation. Design/methodology/approach In this paper a new method is proposed f

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36

Marzen, Sarah E., and James P. Crutchfield. "Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes." Entropy 24, no. 11 (2022): 1675. http://dx.doi.org/10.3390/e24111675.

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Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

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37

Badr, Ahmed, Ahmed Yosri, Sonia Hassini, and Wael El-Dakhakhni. "Coupled Continuous-Time Markov Chain–Bayesian Network Model for Dam Failure Risk Prediction." Journal of Infrastructure Systems 27, no. 4 (2021): 04021041. http://dx.doi.org/10.1061/(asce)is.1943-555x.0000649.

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Zia, Muhammad Azam, Zhongbao Zhang, Guangda Li, Haseeb Ahmad, and Sen Su. "Prediction of Rising Venues in Citation Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 4 (2017): 650–58. http://dx.doi.org/10.20965/jaciii.2017.p0650.

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Prediction of rising stars has become a core issue in data mining and social networks. Prediction of rising venues could unveil rapidly emerging research venues in citation network. The aim of this research is to predict the rising venues. First, we presented five effective prediction features along with their mathematical formulations for extracting rising venues. The underlying features are composed by incorporating the citation count, publications, cited to and cited by information at venue level. For prediction purpose, we employ four machine learning algorithms including Bayesian Network,

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39

Moura, Márcio das Chagas, and Enrique López Droguett. "A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems." Pesquisa Operacional 28, no. 2 (2008): 355–75. http://dx.doi.org/10.1590/s0101-74382008000200011.

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In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the stat

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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dyn

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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dyn

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Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dyn

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Anumasa, Srinivas, and P. K. Srijith. "Latent Time Neural Ordinary Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6010–18. http://dx.doi.org/10.1609/aaai.v36i6.20547.

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Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They provide parameter efficiency and automate the model selection process in deep learning models to some extent. However, they lack the much-required uncertainty modelling and robustness capabilities which are crucial for their use in several real-world applications such as autonomous driving and healthcare. We propose a novel and unique approach to model uncertainty in NODE by considering a distribution over the end-time T

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BOXER, PAUL A. "LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING." International Journal of Computational Intelligence and Applications 01, no. 03 (2001): 273–85. http://dx.doi.org/10.1142/s146902680100024x.

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Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. Th

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Surendra Lakkaraju. "AI-Powered Dynamic Risk Scoring for E-commerce Transactions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 3515–26. https://doi.org/10.32628/cseit251112363.

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This article presents a comprehensive framework for dynamic risk assessment in e-commerce fraud detection using advanced machine learning techniques. The article introduces an innovative approach combining reinforcement learning, Bayesian networks, and real-time processing architectures to address the challenges of modern fraud detection. The implementation demonstrates significant improvements in fraud detection accuracy, reduction in false positives, and enhanced customer experience through adaptive risk scoring mechanisms. The system incorporates sophisticated feature engineering, network-a

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Arya, Shivvrat, Tahrima Rahman, and Vibhav Gogate. "Neural Network Approximators for Marginal MAP in Probabilistic Circuits." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10918–26. http://dx.doi.org/10.1609/aaai.v38i10.28966.

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Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations, such as Bayesian and Markov networks, because PCs can solve marginal inference (MAR) tasks in time that scales linearly in the size of the network. Unfortunately, the most probable explanation (MPE) task and its generalization, the marginal maximum-a-posteriori (MMAP) inference task remain NP-hard in these models. Inspired by the recent work on using neural networks for generating near-optimal sol

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Yu, Yaocheng, Bin Shuai, and Wencheng Huang. "Resilience evaluation of train control on-board system based on multi-dimensional continuous-time Bayesian network." Reliability Engineering & System Safety 246 (June 2024): 110099. http://dx.doi.org/10.1016/j.ress.2024.110099.

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Liu, Jianyu, Linxue Zhao, and Yanlong Mao. "Bayesian regularized NAR neural network based short-term prediction method of water consumption." E3S Web of Conferences 118 (2019): 03024. http://dx.doi.org/10.1051/e3sconf/201911803024.

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With the continuous construction of urban water supply infrastructure, it is extremely urgent to change the management mode of water supply from traditional manual experience to modern and efficient means. The water consumption forecast is the premise of water supply scheduling, and its accuracy also directly affects the effectiveness of water supply scheduling. This paper analyzes the regularity of water consumption time series, establishes a short-term water consumption prediction model based on Bayesian regularized NAR neural network, and compares and evaluates the prediction effect of the

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Stemmer, Marcelo Ricard, Camila Pontes Brito da Costa, Jaqueline Vargas, and Mário Lúcio Roloff. "Artificial Intelligent Systems for Quality Assurance in Small Series Production." Key Engineering Materials 613 (May 2014): 279–87. http://dx.doi.org/10.4028/www.scientific.net/kem.613.279.

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The major challenge faced by a quality assurance (QA) system applied to small series production (SSP) is to guarantee the needed quality level already at the first run. Its called first time right on time. The SSP has some particular characteristics as the great diversity of product types and the continuous introduction of new products. Thus, the QA system has to adapt itself constantly to the new production conditions and support continuous process improvements. In this context, this paper presents the development of intelligent systems for QA in the diagnosis of SSP defects. The two systems

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Kling, Gerhard, Charles Harvey, and Mairi Maclean. "Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables." Organizational Research Methods 20, no. 4 (2015): 770–99. http://dx.doi.org/10.1177/1094428115618760.

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Abstract:

Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker’s qualitative vector autoregression (QVAR) and Lunn, Osorio, and Whittaker’s multivariate probit model to develop a solution to these problems in the form of a qualitati

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