Articles de revues : « Probabilistic Bayesian Network »

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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 component networks can be modeled by another BN. Bayesian Metanetwork is a set of BN, which are put on each other in such a way that conditional or unconditional probability distributions associated with nodes of every previous probabilistic network depend on probability distributions associated with nodes of the next network. We assume parameters (probability distributions) of a BN as random variables and allow conditional dependencies between these probabilities. Several cases of two-level Bayesian Metanetworks were presented, which consist on interrelated predictive and contextual BN models.

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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 conditional interval probabilities and the probabilistic description, as well as the multiplication rules. Furthermore, we propose the method for learning the Bayesian network structure from interval data and the algorithm for corresponding approximate inferences. Experimental results show that our methods are feasible, and we conclude that the Bayesian network with interval probability parameters is the expansion of the general Bayesian network.

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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 producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

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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-IPFP are also proposed: 1) E-IPFP-SMOOTH which deals with the situation where the probabilistic constraints are inconsistent with each other or with the network structure of the given BN; and 2) D-IPFP which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems.

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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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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 classical multi-entity Bayesian network using fuzzy logic, it can be used to represent and reason with probabilistic and vague knowledge simultaneously. Thus, as a language belongs to the proposed approach, this study proposes a promising solution to overcome the weakness of the Probabilistic Ontology Web Language (PR-OWL) based on FzMEBN to allow dealing with vague and probabilistic knowledge in ontologies. The proposed extension is evaluated with a case study in the medical field (diabetes diseases).

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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 in social network users. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, Bayesian network distributed storage and parallel reasoning algorithm is proposed based on Hadoop platform in this paper. Experimental results verify the efficiency and correctness of the algorithm.

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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 neuron network as well as adopting a normalised exponential excitation function. Artificial intelligence prediction based on probabilistic neural networks is verified by MATLAB as having good convergence and fault tolerance as well as data processing capability. The article also analyses the functions of the university intelligent teaching system and realises the optimal design of the university intelligent teaching system.

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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 relationships between variables. The results of this study shows that the probabilistic approach with Bayesian Network can show that capabilities and core competence are the most important variables to produce high performance output.

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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 1) specify special knowledge using the most suitable languages, while reasoning in a uniform engine; 2) make use of pre-existing logical knowledge bases for probabilistic reasoning (to complete the model or minimize potential inconsistencies).

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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 estimation. The forecast estimates computed with the dynamic model are compared with the results achieved with logistic regression combined with multiple regression. The best results were achieved in this case with the combined dynamic net model.Â

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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 fuzzy neural networks.

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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 present a framework called ByNowLife, which is a novel approach for integrating BN with OWL by providing an interface for retrieving probabilistic information through SPARQL queries. ByNowLife catalyzes the integration process by transforming logical information contained in an ontology into a BN and probabilistic information contained in a BN into an ontology. This produces a system with a complete knowledge base. Using ByNowLife, a system that already has separate ontologies and BN knowledge bases can integrate them into a single knowledge base and perform both logical and probabilistic reasoning through it. The integration not only facilitates the unity of reasoning but also has several other advantages, such as ontology enrichment and BN structural adjustment through structural and parameter learning.

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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 approach; however, existing implementations often rely on explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario, or are poorly equipped for automated arbitrary model generation. In this study, we develop a purely probabilistic simulation framework that addresses the demands of statistically sound simulations studies in an unbiased fashion. Additionally, we expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.</p></abstract>

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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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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 retrieval requests.

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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 exact algorithms using distributed network computations, but the problem is NP-hard, and complexity increases significantly with the presence of undirected cycles, the number of discrete states per variable, and the number of variables in the network. This paper describes an approximate method composed of a graph-based evolutionary algorithm that uses nonbinary alphabets, graphs instead of strings, and graph operators to perform abductive inference on multiply connected networks for which systematic search methods are not feasible. The motivation, basis, and adequacy of the method are discussed, and experimental results are presented.

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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 must think about their environment. For example, a robot needs to know about the possible outcomes of its actions, and the system of medical experts needs to know what causes what consequences. Intelligent systems began to use probabilistic methods to deal with the uncertainty of the real world. Instead of building a special system of probabilistic reasoning for each new program, we would like a common framework that would allow probabilistic reasoning in any new program without restoring everything from scratch. This justifies the relevance of the developed genetic algorithm. Bayesian networks, which first appeared in the work of Judas Pearl and his colleagues in the late 1980s, offer just such an independent basis for plausible reasoning.This article presents the genetic algorithm for learning the structure of the Bayesian network that searches the space of the graph, uses mutation and crossover operators. The algorithm can be used as a quick way to learn the structure of a Bayesian network with as few constraints as possible.learn the structure of a Bayesian network with as few constraints as possible.

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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, this paper presents a result on the computation of the probability distribution of a subset of using separately a computation algorithm and D-separation properties. It also shows the uniqueness of the obtained result.

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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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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 and test samples are chosen quiet big: 500 implementations for each image. Such characteristics as training time of the decision rule, recognition time on the test sample, recognition reliability on the test sample, recognition reliability on the training sample were analyzed. In framework of these conditions it was found that the recognition reliability on the test sample in the case of well separated patterns and with any number of the instances is close to 100 percent for both decision rules. The neural network loses 0,1–16 percent to Bayesian decision rule in the recognition reliability on the test sample for poorly separated patterns. The training time of the neural network exceeds the training time of the Bayesian decision rule in 4–5 times and the recognition time – in 4–6 times. As a result, there are no obvious advantages of the probabilistic neural network over the Bayesian decision rule in the problem of Gaussian pattern recognition. The existing generalization of the Bayesian decision rule described in the article is an alternative to the neural network for the case of non-Gaussian patterns.

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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 network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

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Li, Peng, Chaoyang Zhang, Edward J. Perkins, Ping Gong, and Youping Deng. "Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks." BMC Bioinformatics 8, Suppl 7 (2007): S13. http://dx.doi.org/10.1186/1471-2105-8-s7-s13.

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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 approaches for model averaging either severely restrict the structure of the Bayesian network or have only been shown to scale to networks with fewer than 30 random variables. In this paper, we propose a novel approach to model averaging inspired by performance guarantees in approximation algorithms. Our approach has two primary advantages. First, our approach only considers credible models in that they are optimal or near-optimal in score. Second, our approach is more efficient and scales to significantly larger Bayesian networks than existing approaches.

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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 represented by the standardized precipitation index (SPI). The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs), exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.

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Yang, Yi, and John Dalsgaard Sørensen. "Probabilistic Availability Analysis for Marine Energy Transfer Subsystem Using Bayesian Network." Energies 13, no. 19 (2020): 5108. http://dx.doi.org/10.3390/en13195108.

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This research work proposes a novel approach to estimate probabilities of availability states of the energy transfer network in marine energy conversion subsystems, using Bayesian Networks (BNs). The logical interrelationships between units at different level in this network can be understood through qualitative system analysis, which then can be modeled by the fault tree (FT). The FT can be mapped to a corresponding BN, and the condition probabilities of nodes can be determined based on the logic structure. A case study was performed to demonstrate how the mapping is implemented, and the probabilities of availability states were estimated. The results give the probability of each availability state as a function of time, which serves as a basis for choosing the optimal design solution.

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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 semantics of the relations to improve both time and space efficiency, and we prove the correctness of the encodings. We experimentally evaluated our techniques on large-scale real and randomly generated Bayesian networks. On these benchmarks, our techniques gave speedups of up to two orders of magnitude over the best previous approaches for networks with noisy-OR/MAX relations and scaled up to larger networks. As well, our techniques extend the weighted model counting approach for exact inference to networks that were previously intractable for the approach.

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Wu, Jiansong, Rui Zhou, Shengdi Xu, and Zhengwei Wu. "Probabilistic analysis of natural gas pipeline network accident based on Bayesian network." Journal of Loss Prevention in the Process Industries 46 (March 2017): 126–36. http://dx.doi.org/10.1016/j.jlp.2017.01.025.

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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, 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 the shock of vibration signals in the gear system, this study uses statistical factors of vibration signals. Based on these factors, the fault diagnosis is implemented by using Bayesian networks and the results of the two methods, namely, back-propagation neural networks and probabilistic neural network in gear train systems, are compared.

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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 to the Bayesian neural network has not yet been reported in the literature. In addition, most of the research works in the literature for addressing the predictive distribution of neural network output is only for a single target variable, while multiple target variables are rarely involved. In the present paper, for the purpose of probabilistic structural damage detection, Bayesian neural networks with multiple target variables are optimally designed, and the selection of the number of neurons, and the transfer function in the hidden layer, are carried out simultaneously to achieve a neural network architecture with suitable complexity. Furthermore, the nonlinear network function can be approximately linear by assuming the posterior distribution of network parameters is a sufficiently narrow Gaussian, and then the input-dependent covariance matrix of the predictive distribution of network output can be obtained with the Gaussian assumption for the situation of multiple target variables. Structural damage detection is conducted for a steel truss bridge model to verify the proposed method through a set of numerical case studies.

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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 pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. Results We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. Availability and implementation Find the full codebase at https://github.com/gitter-lab/ssps. Supplementary information Supplementary data are available at Bioinformatics online.

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STASSOPOULOU, A., and T. CAELLI. "BUILDING DETECTION USING BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 06 (2000): 715–33. http://dx.doi.org/10.1142/s0218001400000477.

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This paper further explores the uses of Bayesian Networks for detecting buildings from digital orthophotos. This work differs from current research in building detection in so far as it utilizes the ability of Bayesian Networks to provide probabilistic methods for evidence combination and, via training, to determine how such evidence should be weighted to maximize classification. In this vein, then, we have also utilized expert performance to not only configure the network values but also to adapt the feature extraction pre-processing units to fit human behavior as closely as possible. Results from digital orthophotos of the Washington DC area prove that such an approach is feasible, robust and worth further analysis.

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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 skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

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Medeme, Narasimha Rao, and Carlos C. Sun. "Probabilistic Vehicle Identification Techniques for Semiautomated Transportation Security." Transportation Research Record: Journal of the Transportation Research Board 1917, no. 1 (2005): 190–98. http://dx.doi.org/10.1177/0361198105191700121.

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Intelligent transportation systems can play a significant role in transportation security in addition to their traditional roles in transportation operations and management. A multidetector semiautomated vehicle surveillance framework is presented. The objective of the framework is to assist in the search for a vehicle of interest involved with security threats such as terrorism, abduction, or crime. When a vehicle of interest is wanted, this framework can be applied to reduce surveillance data sets and thus reduce time and labor. This system estimates the a posteriori probabilities that indicate the closeness of the match between a vehicle of interest and any vehicle in the search space. This paper explores the use of multidetector fusion of video and inductive loop data by means of a linear fusion model. This system classifies vehicle pairs into possible correct match or incorrect match classes and transforms the problem into the probabilistic domain by using Bayesian neural networks and probabilistic neural networks (PNNs). The use of Bayesian and PNN classifiers assumes equal losses. With Bayesian estimation and generalized regression neural networks, the a posteriori probability is used as a threshold representing unequal losses. A comparison between the traditional Bayesian approaches and the equivalent neural network methods is presented. The use of different feature combinations, methods to balance training data sets, forward sequential search, and combined and uncombined feature approaches is also investigated. Field arterial data from southern California show that, by retaining only 29% of the search space, the framework produces 92% accuracy, which is a promising result.

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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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Boneh, Tal, Gary T. Weymouth, Peter Newham, et al. "Fog Forecasting for Melbourne Airport Using a Bayesian Decision Network." Weather and Forecasting 30, no. 5 (2015): 1218–33. http://dx.doi.org/10.1175/waf-d-15-0005.1.

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Abstract Fog events occur at Melbourne Airport, Melbourne, Victoria, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption, and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast because of the complexity of the physical processes and the impact of local geography and weather elements. Bayesian networks (BNs) are a probabilistic reasoning tool widely used for prediction, diagnosis, and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast decision component and none have been used for operational weather forecasting. A Bayesian decision network [Bayesian Objective Fog Forecast Information Network (BOFFIN)] has been developed for fog forecasting at Melbourne Airport based on 34 years’ worth of data (1972–2005). Parameters were calibrated to ensure that the network had equivalent or better performance to prior operational forecast methods, which led to its adoption as an operational decision support tool. The current study was undertaken to evaluate the operational use of the network by forecasters over an 8-yr period (2006–13). This evaluation shows significantly improved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters because of its skill, visualization, and explanation facilities, and because it offers forecasters control over inputs where a predictor is considered unreliable.

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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, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation. Results A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests. Conclusions We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

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Chukhray, Andrey, and Olena Havrylenko. "The engineering skills training process modeling using dynamic bayesian nets." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (June 2, 2021): 87–96. http://dx.doi.org/10.32620/reks.2021.2.08.

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

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Richardson, Oliver, and Joseph Y. Halpern. "Probabilistic Dependency Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12174–81. http://dx.doi.org/10.1609/aaai.v35i13.17445.

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We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.

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ZHONG, XIAOMIN, and EUGENE SANTOS. "DIRECTING GENETIC ALGORITHMS FOR PROBABILISTIC REASONING THROUGH REINFORCEMENT LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (2000): 167–85. http://dx.doi.org/10.1142/s0218488500000125.

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In this paper, we develop an efficient online approach for belief revision over Bayesian networks by using a reinforcement learning controller to direct a genetic algorithm. The random variables of a Bayesian network can be grouped into several sets reflecting the strong probabilistic correlations between random variables in the group. We build a reinforcement learning controller to identify these groups and recommend the use of "group" mutation and "group" crossover for the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm based on these groupings online. The system then evaluates the performance of the genetic algorithm and continues with reinforcement learning to further tune the controller to search for a better grouping.

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Wang, Hongtao, and Bin Zou. "Probabilistic Load Flow Calculation Considering Correlation Based on Bayesian Network." Electric Power Components and Systems 48, no. 14-15 (2020): 1571–83. http://dx.doi.org/10.1080/15325008.2020.1854378.

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Choi, Seunghee, and Goo Yeon Lee. "Bayesian Network-based Probabilistic Management of Software Metrics for Refactoring." Journal of KIISE 43, no. 12 (2016): 1334–41. http://dx.doi.org/10.5626/jok.2016.43.12.1334.

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Bayraktarli, Yahya Y., and Michael H. Faber. "Bayesian probabilistic network approach for managing earthquake risks of cities." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 5, no. 1 (2011): 2–24. http://dx.doi.org/10.1080/17499511003679907.

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Špačková, Olga, and Daniel Straub. "Dynamic Bayesian Network for Probabilistic Modeling of Tunnel Excavation Processes." Computer-Aided Civil and Infrastructure Engineering 28, no. 1 (2012): 1–21. http://dx.doi.org/10.1111/j.1467-8667.2012.00759.x.

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Kwag, Shinyoung, Abhinav Gupta, and Nam Dinh. "Probabilistic risk assessment based model validation method using Bayesian network." Reliability Engineering & System Safety 169 (January 2018): 380–93. http://dx.doi.org/10.1016/j.ress.2017.09.013.

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