Articles de revues : « Continuous Time Bayesian Networks »

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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 demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.

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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 update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.

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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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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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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 networks. Furthermore, the performance achieved by non-stationary continuous time Bayesian networks is compared to that achieved by state-of-the-art algorithms on four real-world datasets, namely drosophila, saccharomyces cerevisiae, songbird and macroeconomics.

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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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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 then move to structured Markov processes (those arising from state spaces consisting of assignments to variables) including Kronecker, decision-diagram, and continuous-time Bayesian network representations. We provide the first connection between decision-diagrams and continuous-time Bayesian networks.

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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 approximation of the expected information gain. We show that for CTBNs, a semi-analytical expression for this criterion can be calculated for structure and parameter learning. By doing so, we can replace sampling over experimental outcomes by solving the CTBNs master-equation, for which scalable approximations exist. This alleviates the computational burden of integrating over possible experimental outcomes in high-dimensions. We employ this framework in order to recommend interventional sequences. In this context, we extend the CTBN model to conditional CTBNs in order to incorporate interventions. We demonstrate the performance of our criterion on synthetic and real-world data.

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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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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 microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. Availability and implementation The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. Supplementary information Supplementary data are available at Bioinformatics online.

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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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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 performed in polynomial time. Therefore, approximate inference is required. In approximate inference, it is often necessary to trade off accuracy against solution time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks and an algorithm for optimizing its accuracy given a bound on computation time. The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. The trade-off algorithm performs pre-processing to find optimal run-time settings for the extended algorithm. Experimental results for four CG networks compare performance of the extended algorithm with existing algorithms and show the optimal settings for these CG networks.

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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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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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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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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 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.

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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 are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

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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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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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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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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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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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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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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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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 estimation and prediction may be implemented in a general setting using simple recurrent neural networks. The framework is applicable to many situations of common interest, including noisy observations, non-Poisson spike trains (incorporating adaptation), multisensory integration, and state prediction. The optimal network properties are shown to relate to the statistical structure of the environment, and the benefits of adaptation are studied and explicitly demonstrated. Finally, we recover several existing results as appropriate limits of our general setting.

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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 solution approaches. In this paper, we present an alternative approach relying on a continuous time model which avoids the state explosion caused by time stamping in the presence of action concurrency and action duration uncertainty. Time is represented as a continuous random variable. The dependency between state time variables is conveyed by a Bayesian network, which is dynamically generated by a state-based forward-chaining search based on the action descriptions. A generated plan is characterized by a probability of satisfying a goal. The evaluation of this probability is done by making a query the Bayesian network.

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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 that the method can obtain more accurate system reliability and effectively identify the weak parts of the automotive airbag system, to a certain extent compensating for the lack of computational complexity of dynamic Bayesian networks in solving system reliability problems with continuous failure processes.

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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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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 Gaussian densities for each frame and multi-templates for each word are employed to characterize each word pattern. Experimental evaluation of this system with four templates/word and five mixtures/frame, using 53 speakers (28 males, 25 females) and isolated words (10 digits and 30 city names) databases, gave average recognition accuracies of 97.3%, for the speaker-trained mode and 95.7% for the speaker-independent mode, respectively. Comparisons with K-means and DTW algorithms show that the integration of the splitting LVQ and LVQ2 algorithms makes this system well suited to speaker-independent isolated word recognition. A cookbook approach for the determination of parameters in the Hopfield time-alignment network is also described.

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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. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

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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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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, Support Vector Machine, Multilayer Perceptron and Random Forest. Experimental results demonstrate that proposed features set are effective for rising venues prediction. Our empirical analysis spotlights the rising venues that demonstrate the continuous improvement over time and finally become the leading scientific venues.

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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 dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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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 dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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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 dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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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 jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.

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Wu, Si, and Shun-ichi Amari. "Computing with Continuous Attractors: Stability and Online Aspects." Neural Computation 17, no. 10 (2005): 2215–39. http://dx.doi.org/10.1162/0899766054615626.

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Two issues concerning the application of continuous attractors in neural systems are investigated: the computational robustness of continuous attractors with respect to input noises and the implementation of Bayesian online decoding. In a perfect mathematical model for continuous attractors, decoding results for stimuli are highly sensitive to input noises, and this sensitivity is the inevitable consequence of the system's neutral stability. To overcome this shortcoming, we modify the conventional network model by including extra dynamical interactions between neurons. These interactions vary according to the biologically plausible Hebbian learning rule and have the computational role of memorizing and propagating stimulus information accumulated with time. As a result, the new network model responds to the history of external inputs over a period of time, and hence becomes insensitive to short-term fluctuations. Also, since dynamical interactions provide a mechanism to convey the prior knowledge of stimulus, that is, the information of the stimulus presented previously, the network effectively implements online Bayesian inference. This study also reveals some interesting behavior in neural population coding, such as the trade-off between decoding stability and the speed of tracking time-varying stimuli, and the relationship between neural tuning width and the tracking speed.

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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 for the SSER estimation of combinational circuits based on the Bayesian networks (BNs). This allows to factor the joint probability distributions over variables in a circuit graph. The distribution of the initial transient fault pulse is estimated by the pre-characterization tables. Timing signals are propagated by BN theory and the probability distribution of electrical and timing masking are calculated. Findings Simulation results for some benchmark circuits show that the proposed method is accurate with 3.7 percent difference with the Monte-Carlo SPICE simulation and with orders of magnitude improvement in runtime. Originality/value The proposed framework is the scheme giving the low estimation time with plausible accuracy compared to other schemes. The comparison exhibits that the designer can save its estimation time in terms of performance and complexity. The deterministic-based methods also are able to evaluate the SER of combinational circuit, yet in an unacceptable time.

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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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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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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 state probability equations of semi-Markov processes described in terms of transition rates. The numerical procedure is based on the application of Laplace transforms that are inverted by the Gauss quadrature method known as Gauss Legendre. The hybrid model and numerical procedure are illustrated by means of an example of application in the context of fault tolerant systems.

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Yin, Ruyang, Jiping Xing, Pengli Mo, Nan Zheng, and Zhiyuan Liu. "BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems." Electronic Research Archive 30, no. 11 (2022): 3993–4014. http://dx.doi.org/10.3934/era.2022203.

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<abstract> <p>A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&amp;B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&amp;B can potentially aid decision makers in mapping practical network design schemes for large-scale networks.</p> </abstract>

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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 of the ODE solver. The proposed approach, latent time NODE (LT-NODE), treats T as a latent variable and apply Bayesian learning to obtain a posterior distribution over T from the data. In particular, we use variational inference to learn an approximate posterior and the model parameters. Prediction is done by considering the NODE representations from different samples of the posterior and can be done efficiently using a single forward pass. As T implicitly defines the depth of a NODE, posterior distribution over T would also help in model selection in NODE. We also propose, adaptive latent time NODE (ALT-NODE), which allow each data point to have a distinct posterior distribution over end-times. ALT-NODE uses amortized variational inference to learn an approximate posterior using inference networks. We demonstrate the effectiveness of the proposed approaches in modelling uncertainty and robustness through experiments on synthetic and several real-world image classification data.

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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 model. The verification results show that the Bayesian based NAR neural network prediction model has higher adaptability to the water consumption prediction than the standard BP neural network and the Bayesian regularized BP neural network. The prediction accuracy can more accurately reflect the short-term variation of water consumption.

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Zhang, Qianqian, Jianglei Sun, Jing Zhao, Zilin Xia, and Kai Zhang. "Research on the Extraction Method of Book Number Region Based on Bayesian Optimization and Deep Learning." International Journal of Circuits, Systems and Signal Processing 15 (August 27, 2021): 1150–58. http://dx.doi.org/10.46300/9106.2021.15.125.

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The continuous development of artificial intelligence technology has promoted the construction of smart libraries and their intelligent services. In the process of intelligent access to books, the extraction of the requested book number region has become an important part of the process. The requested book number is generally affixed to the bottom of the spine of the book, which is small in size, and the height of the book is not always the same, so it’s difficult to identify. By the way, due to the images’ resolution, shooting angle and other practical problems, the difficulty of the extraction work will be increased. To improve the identification accuracy, in this paper, Bayesian Optimization (BO) and one kind of deep neural networks ‘Faster R-CNN’ are combined for the extraction work mentioned above. The data preparation, network training, optimization variable selection, establishment of BO objective function, optimization training, and network parameter evaluation have been introduced in detail. The performance of the designed algorithm has been tested with actual images of book spines taken in the academy library and compared with several other conventional recognition algorithms. The experimental results show that the requested book number region extraction method based on Bayesian optimization and deep neural network is effective and reliable, and its recognition rate can reach 91.82%, which has advantages in both recognition rate and extraction time compared with other algorithms.

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Hasan, Samiul, and Satish V. Ukkusuri. "Reconstructing Activity Location Sequences From Incomplete Check-In Data: A Semi-Markov Continuous-Time Bayesian Network Model." IEEE Transactions on Intelligent Transportation Systems 19, no. 3 (2018): 687–98. http://dx.doi.org/10.1109/tits.2017.2700481.

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Song, Rongjia, Lei Huang, Weiping Cui, María Óskarsdóttir, and Jan Vanthienen. "Fraud Detection of Bulk Cargo Theft in Port Using Bayesian Network Models." Applied Sciences 10, no. 3 (2020): 1056. http://dx.doi.org/10.3390/app10031056.

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The fraud detection of cargo theft has been a serious issue in ports for a long time. Traditional research in detecting theft risk is expert- and survey-based, which is not optimal for proactive prediction. As we move into a pervasive and ubiquitous paradigm, the implications of external environment and system behavior are continuously captured as multi-source data. Therefore, we propose a novel data-driven approach for formulating predictive models for detecting bulk cargo theft in ports. More specifically, we apply various feature-ranking methods and classification algorithms for selecting an effective feature set of relevant risk elements. Then, implicit Bayesian networks are derived with the features to graphically present the relationship with the risk elements of fraud. Thus, various binary classifiers are compared to derive a suitable predictive model, and Bayesian network performs best overall. The resulting Bayesian networks are then comparatively analyzed based on the outcomes of model validation and testing, as well as essential domain knowledge. The experimental results show that predictive models are effective, with both accuracy and recall values greater than 0.8. These predictive models are not only useful for understanding the dependency between relevant risk elements, but also for supporting the strategy optimization of risk management.

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Wang, Changdong, Hongchun Sun, Rong Zhao, and Xu Cao. "Research on Bearing Fault Diagnosis Method Based on an Adaptive Anti-Noise Network under Long Time Series." Sensors 20, no. 24 (2020): 7031. http://dx.doi.org/10.3390/s20247031.

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In the era of big data, longer time series fault signals will not only be easy to copy and store, but also reduce the labor cost of manual labeling, which can better meet the needs of industrial big data. Aiming to effectively extract the key classification information from a longer time series of bearing vibration signals and achieve high diagnostic accuracy under noise and different load conditions. The one-dimensional adaptive long sequence convolutional network (ALSCN) is proposed. ALSCN can better extract features directly from high-dimensional original signals without manually extracting features and relying on expert knowledge. By adding two improved multi-scale modules, ALSCN can not only extract important features efficiently from noise signals, but also alleviate the problem of losing key information due to continuous down-sampling. Moreover, a Bayesian optimization algorithm is constructed to automatically find the best combination of hyperparameters in ALSCN. Based on two bearing data sets, the model is compared with traditional model such as SVM and deep learning models such as convolutional neural networks (CNN) et al. The results prove that ALSCN has a higher diagnostic accuracy rate on 5120-dimensional sequences under −5 signal to noise ratio (SNR) with better generalization.

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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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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 qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation, and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks, and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.

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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 developed are based on Artificial Intelligence techniques (Bayesian Networks, Expert Systems and Multiagent Systems) that analyze information from the metrological systems (such as a machine vision system) in the SSP line. The goal is to ensure that the cause of a defect will be fixed. The paper will present the context of the SSP, describe two solutions for quality assurance in SSP and will finish with the presentation of the results in the context of a SSP line for Printed Circuit Boards (PCB) mounting.

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