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Journal articles on the topic 'Probabilistic Bayesian Network'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Motomura, Yoichi. "Bayesian Network: Probabilistic Reasoning, Statistical Learning, and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 93–99. http://dx.doi.org/10.20965/jaciii.2004.p0093.

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Bayesian networks are probabilistic models that can be used for prediction and decision-making in the presence of uncertainty. For intelligent information processing, probabilistic reasoning based on Bayesian networks can be used to cope with uncertainty in real-world domains. In order to apply this, we need appropriate models and statistical learning methods to obtain models. We start by reviewing Bayesian network models, probabilistic reasoning, statistical learning, and related researches. Then, we introduce applications for intelligent information processing using Bayesian networks.

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TERZIYAN, VAGAN. "A BAYESIAN METANETWORK." International Journal on Artificial Intelligence Tools 14, no. 03 (2005): 371–84. http://dx.doi.org/10.1142/s0218213005002156.

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Bayesian network (BN) is known to be one of the most solid probabilistic modeling tools. The theory of BN provides already several useful modifications of a classical network. Among those there are context-enabled networks such as multilevel networks or recursive multinets, which can provide separate BN modelling for different combinations of contextual features' values. The main challenge of this paper is the multilevel probabilistic meta-model (Bayesian Metanetwork), which is an extension of traditional BN and modification of recursive multinets. It assumes that interoperability between comp

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LIU, WEI-YI, and KUN YUE. "BAYESIAN NETWORK WITH INTERVAL PROBABILITY PARAMETERS." International Journal on Artificial Intelligence Tools 20, no. 05 (2011): 911–39. http://dx.doi.org/10.1142/s0218213011000449.

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Interval data are widely used in real applications to represent the values of quantities in uncertain situations. However, the implied probabilistic causal relationships among interval-valued variables with interval data cannot be represented and inferred by general Bayesian networks with point-based probability parameters. Thus, it is desired to extend the general Bayesian network with effective mechanisms of representation, learning and inference of probabilistic causal relationships implied in interval data. In this paper, we define the interval probabilities, the bound-limited weak conditi

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Herskovits, E. H., and G. F. Cooper. "Algorithms for Bayesian Belief-Network Precomputation." Methods of Information in Medicine 30, no. 02 (1991): 81–89. http://dx.doi.org/10.1055/s-0038-1634820.

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AbstractBayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.We first present the necessary background, and then present algorithms for producin

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VÉRONIQUE, DELCROIX, MAALEJ MOHAMED-AMINE, and PIECHOWIAK SYLVAIN. "BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES." International Journal on Artificial Intelligence Tools 16, no. 03 (2007): 417–33. http://dx.doi.org/10.1142/s0218213007003345.

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Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.

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PENG, YUN, ZHONGLI DING, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REVISION WITH PROBABILISTIC CONSTRAINTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 03 (2012): 317–37. http://dx.doi.org/10.1142/s021848851250016x.

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This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability distribution of the revised BN is as close as possible to that of the original BN. Two variations of E-IPF

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Riali, Ishak, Messaouda Fareh, and Hafida Bouarfa. "Fuzzy Probabilistic Ontology Approach." International Journal on Semantic Web and Information Systems 15, no. 4 (2019): 1–20. http://dx.doi.org/10.4018/ijswis.2019100101.

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In spite of the undeniable success of the ontologies, where they have been widely applied successfully to represent the knowledge in lots of real-world problems, they cannot represent and reason with uncertain knowledge which inherently appears in most domains. To cope with this issue, this article presents a new approach for dealing with rich-uncertainty domains. In fact, it is mainly based on integrating hybrid models which combine both fuzzy logic and Bayesian networks. On the other hand, the Fuzzy multi-entity Bayesian network (FzMEBN) proposed as a hybrid model which enhances the classica

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Singh, Vikash, Matthew Khanzadeh, Vincent Davis, et al. "Bayesian Binary Search." Algorithms 18, no. 8 (2025): 452. https://doi.org/10.3390/a18080452.

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We present Bayesian Binary Search (BBS), a novel framework that bridges statistical learning theory/probabilistic machine learning and binary search. BBS utilizes probabilistic methods to learn the underlying probability density of the search space. This learned distribution then informs a modified bisection strategy, where the split point is determined by probability density rather than the conventional midpoint. This learning process for search space density estimation can be achieved through various supervised probabilistic machine learning techniques (e.g., Gaussian Process Regression, Bay

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Zhu, Xianyou, and Songlin Tang. "Design of an Artificial Intelligence Algorithm Teaching System for Universities Based on Probabilistic Neuronal Network Model." Scientific Programming 2022 (April 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/4131058.

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Intelligence is gradually becoming an important tool for solving difficult problems with the development of computers. This article takes the design of university teaching systems as the research context to establish an artificial intelligence network research and learning platform. A probabilistic process neuron network model is proposed, which combines the Bayesian probabilistic classification mechanism with the dynamic signal processing method of process neuron networks, and achieves dynamic classification based on Bayesian rules by adding a pattern unit layer to the feed-forward process ne

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Su, Jie, Jun Li, and Jifeng Chen. "Probabilistic Graph Model Mining User Affinity in Social Networks." International Journal of Web Services Research 18, no. 3 (2021): 22–41. http://dx.doi.org/10.4018/ijwsr.2021070102.

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In social networks, discovery of user similarity is the basis of social media data analysis. It can be applied to user-based product recommendations and inference of user relationship evolution in social networks. In order to effectively describe the complex correlation and uncertainty for social network users, the accuracy of similarity discovery is improved theoretically for massive social network users. Based on the Bayesian network probability map model, network topological structure is combined with the dependency between users, and an effective method is proposed to discover similarity i

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Wang, Jingsong, and Marco Valtorta. "A Framework for Integration of Logical and Probabilistic Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1822–23. http://dx.doi.org/10.1609/aaai.v25i1.8048.

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Integrating the expressive power of first-order logic with the ability of probabilistic reasoning of Bayesian networks has attracted the interest of many researchers for decades. We present an approach to integration that translates logical knowledge into Bayesian networks and uses Bayesian network composition to build a uniform representation that supports both logical and probabilistic reasoning. In particular, we propose a new way of translation of logical knowledge, relation search. Through the use of the proposed framework, without learning new languages or tools, modelers are allowed to

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Baskara Nugraha, I. Gusti Bagus, Imaniar Ramadhani, and Jaka Sembiring. "Probabilistic Inference Hybrid IT Value Model Using Bayesian Network." International Journal on Electrical Engineering and Informatics 12, no. 4 (2020): 770–85. http://dx.doi.org/10.15676/ijeei.2020.12.4.5.

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In this study, we propose probabilistic inference model on a hybrid IT value model using Bayesian Network (BN) that represents uncertain relationships between 13 variables of the model. Those variables are performance, market, innovation, IT support, core competence, capabilities, knowledge, human resources, IT development, IT resources, capital, labor, and IT spending. The relationships between variables in the model are determined using probabilistic approach, including the structure, nature, and direction of relationships. We derive a probabilistic graphical model and measure the relationsh

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Bidyuk, Peter, Aleksander Peter Gozhjy, and Alexandr T. Rofymchuk. "Forecasting based on Bayesian type models." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 3 (2015): 6570–84. http://dx.doi.org/10.24297/ijct.v15i3.1672.

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A review of some Bayesian data analysis models is proposed, namely the models with one and several parameters. A methodology is developed for probabilistic models construction in the form of Bayesian networks using statistical data and expert estimates. The methodology provides a possibility for constructing high adequacy probabilistic models for solving the problems of classification and forecasting. An integrated dynamic network model is proposed that is based on combination of probabilistic and regression approaches; the model is distinguished with a possibility for multistep forecasts esti

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SAITO, KENJI, HIROYUKI SHIOYA, and TSUTOMU DA-TE. "A TREATMENT OF USEFULNESS OF KEYWORDS IN FUZZY REQUESTS FOR AN INFORMATION RETRIEVAL SYSTEM WITH BAYESIAN NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (1999): 399–406. http://dx.doi.org/10.1142/s0218488599000350.

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We improve a document retrieval method based on the so-called maximum entropy principle proposed by Cooper, and show how to implement this system on a Bayesian network. A Bayesian network is a probabilistic model for expressing probabilistic relations among random variables. We show advantages of a document retrieval system on a Bayesian network in comparison with Cooper's system. The original document retrieval system based on the maximum entropy principle has a drawback: a result of retrieval can not be obtained in some cases. In this paper, we resolve this drawback by fuzzification of user

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Kozhomberdieva, Gulnara I., Dmitry P. Burakov, and Georgii A. Khamchichev. "THE STRUCTURE OF A NEURO-FUZZY NETWORK BASED ON BAYESIAN LOGICAL-PROBABILISTIC MODEL." SOFT MEASUREMENTS AND COMPUTING 12, no. 61 (2022): 52–64. http://dx.doi.org/10.36871/2618-9976.2022.12.004.

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The article presents a multilayer structure of a neurofuzzy network based on the Bayesian logicalprobabilistic model of fuzzy inference, previously proposed, researched and implemented by the authors. A brief description of the Bayesian logicalprobabilistic model is given, an example of setting up a neurofuzzy network for solving a fuzzy inference problem is presented. The example shows which network parameters can be used for its training. According to the authors, the proposed network structure with three parametric layers is comparable to the wellknown Takagi– Sugeno–Kang and Wang–Mendel fu

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Zhang, San Tong. "Research of Fault Diagnosis Based on Bayesian Network for Air Brake System." Advanced Materials Research 143-144 (October 2010): 629–33. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.629.

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A method for solving the fault diagnosis problem of air brake system based on probabilistic approach is presented. The fault diagnosis model based on Bayesian network was built for the uncertainty characteristic of fault in the air brake system. Through evaluating the characteristic of Bayesian networks in the diagnosis inference and model expression, it is demonstrated that this method can solve the uncertain problems in fault diagnosis. The test result has shown that the Bayesian network model is effective in fault diagnosis of the air brake system.

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Setiawan, Foni, Eko Budiardjo, and Wahyu Wibowo. "ByNowLife: A Novel Framework for OWL and Bayesian Network Integration." Information 10, no. 3 (2019): 95. http://dx.doi.org/10.3390/info10030095.

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An ontology-based system can currently logically reason through the Web Ontology Language Description Logic (OWL DL). To perform probabilistic reasoning, the system must use a separate knowledge base, separate processing, or third-party applications. Previous studies mainly focus on how to represent probabilistic information in ontologies and perform reasoning through them. These approaches are not suitable for systems that already have running ontologies and Bayesian network (BN) knowledge bases because users must rewrite the probabilistic information contained in a BN into an ontology. We pr

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Gogoshin, Grigoriy, Sergio Branciamore, and Andrei S. Rodin. "Synthetic data generation with probabilistic Bayesian Networks." Mathematical Biosciences and Engineering 18, no. 6 (2021): 8603–21. http://dx.doi.org/10.3934/mbe.2021426.

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<abstract><p>Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect the underlying biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The last is arguably the most comprehensive appro

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Zheng, Chenyiqiu. "A comprehensive review of probabilistic and statistical methods in social network sentiment analysis." Advances in Engineering Innovation 16, no. 3 (2025): 38–43. https://doi.org/10.54254/2977-3903/2025.21918.

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In the era of rapid digital transformation, social networks generate huge amounts of textual data every day, making sentiment analysis an essential tool for understanding public opinion. This study focuses on the application of probabilistic and statistical methods to sentiment analysis in social networks, highlighting their effectiveness in dealing with uncertainty and modeling the distribution of emotions. The main objective is to evaluate the role of Nave Bayesian (NB), Hidden Markov models (HMMs), and Bayesian networks in emotion classification, emotion propagation, and dynamic emotion tra

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Levenchuk, Liudmyla. "The Bayesian approach to analysis of financial operational risk." ScienceRise, no. 2 (April 30, 2022): 11–20. https://doi.org/10.21303/2313-8416.2022.002377.

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The article provides a short overview of methods for constructing mathematical models in the form of Bayesian Networks for modeling operational risks under conditions of uncertainty. Let's provide the sequence of actions necessary for creating a model in the form of the network, methods for computing a probabilistic output in BN, and give examples of using the tool to solve practical problems of operational financial risk estimation. The study results can be used by financial institutions as a tool for resolving specific practical issues of risk estimation. The object of research: methods for

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Koblitz, A. Ryo, Lorenzo Maggi, and Matthew Andrews. "Reducing the Environmental Impact of Wireless Communication via Probabilistic Machine Learning." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 89–93. http://dx.doi.org/10.1609/aaaiss.v2i1.27654.

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Machine learning methods are increasingly adopted in communications problems, particularly those arising in next generation wireless settings. Though seen as a key climate mitigation and societal adaptation enabler, communications related energy consumption is high and is expected to grow in future networks in spite of anticipated efficiency gains in 6G due to exponential communications traffic growth. To make meaningful climate mitigation impact in the communications sector, a mindset shift away from maximizing throughput at all cost and towards prioritizing energy efficiency is needed. Moreo

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Salmani, Bahare, and Joost-Pieter Katoen. "Automatically Finding the Right Probabilities in Bayesian Networks." Journal of Artificial Intelligence Research 77 (August 27, 2023): 1637–96. http://dx.doi.org/10.1613/jair.1.14044.

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This paper presents alternative techniques for inference on classical Bayesian networks in which all probabilities are fixed, and for synthesis problems when conditional probability tables (CPTs) in such networks contain symbolic parameters rather than concrete probabilities. The key idea is to exploit probabilistic model checking as well as its recent extension to parameter synthesis techniques for parametric Markov chains. To enable this, the Bayesian networks are transformed into Markov chains and their objectives are mapped onto probabilistic temporal logic formulas. For exact inference, w

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Fareh, Messaouda, Ishak Riali, Hafsa Kherbache, and Marwa Guemmouz. "Probabilistic reasoning for diagnosis prediction of Coronavirus disease based on probabilistic ontology." Computer Science and Information Systems, no. 00 (2023): 35. http://dx.doi.org/10.2298/csis220829035f.

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The novel Coronavirus has been declared a pandemic by the World Health Organization (WHO). Predicting the diagnosis of COVID-19 is essential for disease cure and control. The paper?s main aim is to predict the COVID-19 diagnosis using probabilistic ontologies to address the randomness and incomplete ness of knowledge. Our approach begins with constructing the entities, attributes, and relationships of COVID-19 ontology, by extracting symptoms and risk factors. The probabilistic components of COVID-19 ontology are developed by creating a Multi-Entity Bayesian Network, then determining its compo

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Smail, Linda. "Uniqueness of the Level Two Bayesian Network Representing a Probability Distribution." International Journal of Mathematics and Mathematical Sciences 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/845398.

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Bayesian Networks are graphic probabilistic models through which we can acquire, capitalize on, and exploit knowledge. they are becoming an important tool for research and applications in artificial intelligence and many other fields in the last decade. This paper presents Bayesian networks and discusses the inference problem in such models. It proposes a statement of the problem and the proposed method to compute probability distributions. It also uses D-separation for simplifying the computation of probabilities in Bayesian networks. Given a Bayesian network over a family of random variables

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Rohit, B. Kaliwal, and L. Deshpande Santosh. "Efficiency of Probabilistic Network Model for Assessment in E-Learning System." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 562–66. https://doi.org/10.35940/ijrte.C4635.099320.

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The knowledge acquirement by the learner is a major assignment of an E-Learning framework. Evaluation is required in order to adapt knowledge resources and task to learner ability. Assessment provides learner’s an approach to evaluate the skills gained through the e-learning domain they are accessing. A dissimilar method can be used to assess the information acquirement, such as probabilistic Bayesian Network model. A Bayesian Network is a graphical representation of the probabilistic relationships of a complex system. This network can be used for reasoning with uncertainty. Bayesian Net

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Faggian, Claudia, Daniele Pautasso, and Gabriele Vanoni. "Higher Order Bayesian Networks, Exactly." Proceedings of the ACM on Programming Languages 8, POPL (2024): 2514–46. http://dx.doi.org/10.1145/3632926.

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Bayesian networks are graphical first-order probabilistic models that allow for a compact representation of large probability distributions, and for efficient inference, both exact and approximate. We introduce a higher-order programming language, in the idealized form of a lambda-calculus, which we prove sound and complete w.r.t. Bayesian networks: each Bayesian network can be encoded as a term, and conversely each (possibly higher-order and recursive) program of ground type compiles into a Bayesian network. The language allows for the specification of recursive probability models and hierarc

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Rojas-Guzmán, Carlos, and Mark A. Kramer. "An Evolutionary Computing Approach to Probabilistic Reasoning on Bayesian Networks." Evolutionary Computation 4, no. 1 (1996): 57–85. http://dx.doi.org/10.1162/evco.1996.4.1.57.

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Bayesian belief networks can be used to represent and to reason about complex systems with uncertain or incomplete information. Bayesian networks are graphs capable of encoding and quantifying probabilistic dependence and conditional independence among variables. Diagnostic reasoning, also referred to as abductive inference, determining the most probable explanation (MPE), or finding the maximum a posteriori instantiation (MAP), involves determining the global most probable system description given the values of any subset of variables. In some cases abductive inference can be performed with e

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Wang, Qi-Ang, Ao-Wen Lu, Yi-Qing Ni, Jun-Fang Wang, and Zhan-Guo Ma. "Bayesian Network in Structural Health Monitoring: Theoretical Background and Applications Review." Sensors 25, no. 12 (2025): 3577. https://doi.org/10.3390/s25123577.

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With accelerated urbanization and aging infrastructure, the safety and durability of civil engineering structures face significant challenges, making structural health monitoring (SHM) a critical approach to ensuring engineering safety. The Bayesian network, as a probabilistic reasoning tool, offers a novel technological pathway for SHM due to its strengths in handling uncertainties and multi-source data fusion. This study systematically reviews the core applications of the Bayesian network in SHM, including damage prediction, data fusion, uncertainty modeling, and decision support. By integra

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Mukha, V. S. "Comparative numerical analysis of Bayesian decision rule and probabilistic neural network for pattern recognition." Doklady BGUIR 19, no. 7 (2021): 13–21. http://dx.doi.org/10.35596/1729-7648-2021-19-7-13-21.

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At present, neural networks are increasingly used to solve many problems instead of traditional methods for solving them. This involves comparing the neural network and the traditional method for specific tasks. In this paper, computer modeling of the Bayesian decision rule and the probabilistic neural network is carried out in order to compare their operational characteristics for recognizing Gaussian patterns. Recognition of four and six images (classes) with the number of features from 1 to 6 was simulated in cases where the images are well and poorly separated. The sizes of the training an

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Salii, Anna. "Methods of Learning the Structure of the Bayesian Network." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 56–59. http://dx.doi.org/10.18523/2617-3808.2021.4.56-59.

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Sometimes in practice it is necessary to calculate the probability of an uncertain cause, taking into account some observed evidence. For example, we would like to know the probability of a particular disease when we observe the patient’s symptoms. Such problems are often complex with many interrelated variables. There may be many symptoms and even more potential causes. In practice, it is usually possible to obtain only the inverse conditional probability, the probability of evidence giving the cause, the probability of observing the symptoms if the patient has the disease.Intelligent systems

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Cardoso, Wandercleiton, and Renzo di Felice. "Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning." International Journal of Advances in Intelligent Informatics 7, no. 3 (2021): 268. http://dx.doi.org/10.26555/ijain.v7i3.771.

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The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural net

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Roland Abi. "Bayesian Network Modeling for Probabilistic Reasoning and Risk Assessment in Large-Scale Industrial Datasets." International Journal of Science and Research Archive 15, no. 3 (2025): 587–607. https://doi.org/10.30574/ijsra.2025.15.3.1765.

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In complex industrial environments, uncertainty is inherent in decision-making due to dynamic operating conditions, sensor variability, and the vast heterogeneity of data sources. Traditional deterministic models often fall short in capturing the probabilistic dependencies and hidden causal relationships that characterize these systems. As industries increasingly adopt data-driven strategies, probabilistic reasoning frameworks such as Bayesian Network (BN) modeling have gained prominence for their ability to encode domain knowledge, handle incomplete information, and support transparent infere

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Liao, Zhenyu A., Charupriya Sharma, James Cussens, and Peter Van Beek. "Finding All Bayesian Network Structures within a Factor of Optimal." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7892–99. http://dx.doi.org/10.1609/aaai.v33i01.33017892.

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A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as an optimization problem using the well-known score-andsearch approach. However, selecting a single model (i.e., the best scoring BN) can be misleading or may not achieve the best possible accuracy. An alternative to committing to a single model is to perform some form of Bayesian or frequentist model averaging, where the space of possible BNs is sampled or enumerated in some fashion. Unfortunately, existing approach

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Li, W., P. Poupart, and P. Van Beek. "Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference." Journal of Artificial Intelligence Research 40 (April 19, 2011): 729–65. http://dx.doi.org/10.1613/jair.3232.

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Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference. In this paper, we present techniques for improving this approach for Bayesian networks with noisy-OR and noisy-MAX relations---two relations that are widely used in practice as they can dramatically reduce the number of probabilities one needs to specify. In particular, we present two SAT encodings for noisy-OR and two encodings for noisy-MAX that exploit the structure or semantic

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Shin, Ji Yae, Muhammad Ajmal, Jiyoung Yoo, and Tae-Woong Kim. "A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook." Advances in Meteorology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9472605.

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Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were

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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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Carlos, Ramírez, and De la Torre-Gea Guillermo. "Perception of Security in Mexico through Bayesian Networks." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1244–52. https://doi.org/10.31142/ijtsrd10702.

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The present investigation shows an analysis of the perception of security by the population, using the Bayes probabilistic method, based on open data from the Survey of Victimization and Perception of Public Safety ENVIPE . Through a Bayesian network composed of variables that are mostly interrelated, coupled with probabilistic inferences that in turn project on others, with the purpose of analyzing their probabilistic behaviors by originating actions that should be considered as preventive measures in the face of insecurity. As a result of this study, people who have home surveillance systems

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Jeong, Jihoon, and Jongsoo Lee. "Probabilistic Failure Analysis of Door System Using Bayesian Network." Korean Journal of Computational Design and Engineering 26, no. 1 (2021): 40–49. http://dx.doi.org/10.7315/cde.2021.040.

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dos Reis, B. R., C. B. Gleason, and R. R. White. "O18 Bayesian network probabilistic modeling for understanding rumen dynamics." Animal - science proceedings 13, no. 3 (2022): 265–66. http://dx.doi.org/10.1016/j.anscip.2022.07.028.

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Afrin, Tanzina, and Nita Yodo. "A probabilistic estimation of traffic congestion using Bayesian network." Measurement 174 (April 2021): 109051. http://dx.doi.org/10.1016/j.measurement.2021.109051.

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Zhou, Kai, and Jiong Tang. "Probabilistic Gear Fault Diagnosis Using Bayesian Convolutional Neural Network." IFAC-PapersOnLine 55, no. 37 (2022): 795–99. http://dx.doi.org/10.1016/j.ifacol.2022.11.279.

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Das, Tanmoy, and Deepankar Choudhury. "Bayesian Network Based Probabilistic Modelling of Earthquake Induced Landslides." Japanese Geotechnical Society Special Publication 10, no. 13 (2024): 372–77. http://dx.doi.org/10.3208/jgssp.v10.os-2-04.

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Sakthivel, Ranganathan, and Gundala Vijayalakshmi. "Reliability Analysis for Multistate Consecutive k-out-of-n: F System Using Bayesian Network." Mathematical Modelling of Engineering Problems 10, no. 2 (2023): 613–20. http://dx.doi.org/10.18280/mmep.100231.

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This paper aims to determine the reliability of a complex system using a Bayesian network. A Bayesian network (BN) is a probabilistic graphical model that represents knowledge about an uncertain domain where each component corresponds to a random variable and each edge represents the corresponding conditional probability. Bayesian network is used to estimate the multistate consecutive k-out-of-n: F system reliability. This paper presents the Bayesian network construction and the reliability of the proposed system. The reliability of linear and circular multistate consecutive k-out-of-n: F syst

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Wang, Chun Chieh, Yuan Kang, and Chin Chi Liao. "Using Bayesian Networks in Gear Fault Diagnosis." Applied Mechanics and Materials 284-287 (January 2013): 2416–20. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2416.

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In rotary machinery, the symptoms of vibration signals in the frequency domain have been used as inputs for neural networks and diagnosis results can be obtained by network computation. However, in gear or rolling bearing systems, it is difficult to extract symptoms from vibration signals in the frequency domain where shock vibration signals are present, and neural networks do not provide satisfactory diagnosis results without adequate training samples. Bayesian networks provide an effective approach for fault diagnosis in cases given uncertain knowledge and incomplete information. To classify

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Yin, Tao, and Hong-ping Zhu. "Probabilistic Damage Detection of a Steel Truss Bridge Model by Optimally Designed Bayesian Neural Network." Sensors 18, no. 10 (2018): 3371. http://dx.doi.org/10.3390/s18103371.

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Excellent pattern matching capability makes artificial neural networks (ANNs) a very promising approach for vibration-based structural health monitoring (SHM). The proper design of the network architecture with the suitable complexity is vital to the ANN-based structural damage detection. In addition to the number of hidden neurons, the type of transfer function used in the hidden layer cannot be neglected for the ANN design. Neural network learning can be further presented in the framework of Bayesian statistics, but the issues of selection for the hidden layer transfer function with respect

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Zmeev, D. O., O. A. Zmeev, L. S. Ivanova, and V. I. Freydin. "Development of a subsystem to use Bayesian networks in a decision support system for software development management." Proceedings of Tomsk State University of Control Systems and Radioelectronics 25, no. 3 (2022): 52–56. http://dx.doi.org/10.21293/1818-0442-2022-25-3-52-56.

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Bayesian networks are currently a popular tool for solving various problems, including creating decision support systems. This paper proposes a tool for creating a Bayesian network and direct probabilistic inference. The specificity of the problem consists in working with large networks (more than 1000 nodes) with a large number of parent nodes at one node (15 or more). The tool is integrated with Redmine and allows you to calculate the probability of a manager's error when determining the current state of the project.

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Xu, Liyu, and Xinsheng Liu. "Probabilistic modeling and numerical simulation of neural circuits for multisensory integration." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 522–28. http://dx.doi.org/10.54097/hset.v70i.13946.

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People in real life receive stimulus information through various senses, and the process by which the brain integrates this information is called multisensory integration. Multisensory integration is an important branch of neuroscience, and the research on its neural mechanism holds significant application value to the development of artificial intelligence such as designing intelligent robots. Researches suggests that the brain likely employs Bayesian rules to integrate information and make judgments. In machine learning, neural networks based on Spike-Timing-Dependent Plasticity (STDP) have

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Ding, Huitong, Ning An, Rhoda Au, et al. "Exploring the Hierarchical Influence of Cognitive Functions for Alzheimer Disease: The Framingham Heart Study." Journal of Medical Internet Research 22, no. 4 (2020): e15376. http://dx.doi.org/10.2196/15376.

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Background Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests. Objective This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors. Methods Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechs

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Heijer, C. (Kees) Den, Dirk T. J. A. Knipping, Nathaniel G. Plant, Jaap S. M. Van Thiel de Vries, Fedor Baart, and Pieter H. A. J. M. Van Gelder. "IMPACT ASSESSMENT OF EXTREME STORM EVENTS USING A BAYESIAN NETWORK." Coastal Engineering Proceedings 1, no. 33 (2012): 4. http://dx.doi.org/10.9753/icce.v33.management.4.

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This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a s

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Merrell, David, and Anthony Gitter. "Inferring signaling pathways with probabilistic programming." Bioinformatics 36, Supplement_2 (2020): i822—i830. http://dx.doi.org/10.1093/bioinformatics/btaa861.

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Abstract Motivation Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling

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