Relevant bibliographies by topics / Probabilistic Bayesian Network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probabilistic Bayesian Network'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "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 (March 20, 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 (June 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 (October 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 (May 17, 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 (June 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 (October 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 (July 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 (December 31, 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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Dissertations / Theses on the topic "Probabilistic Bayesian Network"

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Sahin, Elvan. "Discrete-Time Bayesian Networks Applied to Reliability of Flexible Coping Strategies of Nuclear Power Plants." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103817.

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The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risky situations in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by Nuclear Energy Institute (NEI) to manage the challenge of BDBEEs and to enhance reactor safety against extended station blackout (SBO). To assess the effectiveness of FLEX strategies, probabilistic risk assessment (PRA) methods can be used to calculate the reliability of such systems. Due to the uniqueness of FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. Therefore, a suitable method is needed to analyze the reliability of FLEX systems in nuclear reactors. This thesis investigates the effectiveness and applicability of Bayesian networks (BNs) and Discrete-Time Bayesian Networks (DTBNs) in the reliability analysis of FLEX equipment that is utilized to reduce the risk in nuclear power plants. To this end, the thesis compares BNs with two other reliability assessment methods: Fault Tree (FT) and Markov chain (MC). Also, it is shown that these two methods can be transformed into BN to perform the reliability analysis of FLEX systems. The comparison of the three reliability methods is shown and discussed in three different applications. The results show that BNs are not only a powerful method in modeling FLEX strategies, but it is also an effective technique to perform reliability analysis of FLEX equipment in nuclear power plants.
Master of Science
Some external events like earthquakes, flooding, and severe wind, may cause damage to the nuclear reactors. To reduce the consequences of these damages, the Nuclear Energy Institute (NEI) has proposed mitigating strategies known as FLEX (Diverse and Flexible Coping Strategies). After the implementation of FLEX in nuclear power plants, we need to analyze the failure or success probability of these engineering systems through one of the existing methods. However, the existing methods are limited in analyzing the dependencies among components in complex systems. Bayesian networks (BNs) are a graphical and quantitative technique that is utilized to model dependency among events. This thesis shows the effectiveness and applicability of BNs in the reliability analysis of FLEX strategies by comparing it with two other reliability analysis tools, known as Fault Tree Analysis and Markov Chain. According to the reliability analysis results, BN is a powerful and promising method in modeling and analyzing FLEX strategies.
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Yoo, Keunyoung. "Probabilistic SEM : an augmentation to classical Structural equation modelling." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66521.

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Structural equation modelling (SEM) is carried out with the aim of testing hypotheses on the model of the researcher in a quantitative way, using the sampled data. Although SEM has developed in many aspects over the past few decades, there are still numerous advances which can make SEM an even more powerful technique. We propose representing the nal theoretical SEM by a Bayesian Network (BN), which we would like to call a Probabilistic Structural Equation Model (PSEM). With the PSEM, we can take things a step further and conduct inference by explicitly entering evidence into the network and performing di erent types of inferences. Because the direction of the inference is not an issue, various scenarios can be simulated using the BN. The augmentation of SEM with BN provides signi cant contributions to the eld. Firstly, structural learning can mine data for additional causal information which is not necessarily clear when hypothesising causality from theory. Secondly, the inference ability of the BN provides not only insight as mentioned before, but acts as an interactive tool as the `what-if' analysis is dynamic.
Mini Dissertation (MCom)--University of Pretoria, 2018.
Statistics
MCom
Unrestricted
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Zhao, Wenyu. "A Probabilistic Approach for Prognostics of Complex Rotary Machinery Systems." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1423581651.

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Björkman, Peter. "Probabilistic Safety Assessment using Quantitative Analysis Techniques : Application in the Heavy Automotive Industry." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-163262.

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Safety is considered as one of the most important areas in future research and development within the automotive industry. New functionality, such as driver support and active/passive safety systems are examples where development mainly focuses on safety. At the same time, the trend is towards more complex systems, increased software dependence and an increasing amount of sensors and actuators, resulting in a higher risk associated with software and hardware failures. In the area of functional safety, standards such as ISO 26262 assess safety mainly focusing on qualitative assessment techniques, whereas usage of quantitative techniques is a growing area in academic research. This thesis considers the field functional safety, with the emphasis on how hardware and software failure probabilities can be used to quantitatively assess safety of a system/function. More specifically, this thesis presents a method for quantitative safety assessment using Bayesian networks for probabilistic modeling. Since the safety standard ISO 26262 is becoming common in the automotive industry, the developed method is adjusted to use information gathered when implementing this standard. Continuing the discussion about safety, a method for modeling faults and failures using Markov models is presented. These models connect to the previous developed Bayesian network and complete the quantitative safety assessment. Furthermore, the potential for implementing the discussed models in the Modelica language is investigated, aiming to find out if models such as these could be useful in practice to simplify design work, in order to meet future safety goals.
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Quer, Giorgio. "Optimization of Cognitive Wireless Networks using Compressive Sensing and Probabilistic Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421992.

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In-network data aggregation to increase the efficiency of data gathering solutions for Wireless Sensor Networks (WSNs) is a challenging task. In the first part of this thesis, we address the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a Data Collection Point (DCP). We exploit Principal Component Analysis (PCA) to learn the relevant statistical characteristics of the signals of interest at the DCP. Then, at the DCP we use this knowledge to design a matrix required by the recovery techniques, that exploit convex optimization (Compressive Sensing, CS) in order to recover the whole signal sensed by the WSN from a small number of samples gathered. In order to integrate this monitoring model in a compression/recovery framework, we apply the logic of the cognition paradigm: we first observe the network, then we learn the relevant statistics of the signals, we apply it to recover the signal and to make decisions, that we effect through the control loop. This compression/recovery framework with a feedback control loop is named "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). The whole framework is designed for a WSN architecture, called WSN-control, that is accessible from the Internet. We also analyze with a Bayesian approach the whole framework to justify theoretically the choices made in our protocol design. The second part of the thesis deals with the application of the cognition paradigm to the optimization of a Wireless Local Area Network (WLAN). In this work, we propose an architecture for cognitive networking that can be integrated with the existing layered protocol stack. Specifically, we suggest the use of a probabilistic graphical model for modeling the layered protocol stack. In particular, we use a Bayesian Network (BN), a graphical representation of statistical relationships between random variables, in order to describe the relationships among a set of stack-wide protocol parameters and to exploit this cross-layer approach to optimize the network. In doing so, we use the knowledge learned from the observation of the data to predict the TCP throughput in a single-hop wireless network and to infer the future occurrence of congestion at the TCP layer in a multi-hop wireless network. The approach followed in the two main topics of this thesis consists of the following phases: (i) we apply the cognition paradigm to learn the specific probabilistic characteristics of the network, (ii) we exploit this knowledge acquired in the first phase to design novel protocol techniques, (iii) we analyze theoretically and through extensive simulation such techniques, comparing them with other state of the art techniques, and (iv) we evaluate their performance in real networking scenarios.
La combinazione delle informazioni nelle reti di sensori wireless è una soluzione promettente per aumentare l'efficienza delle techiche di raccolta dati. Nella prima parte di questa tesi viene affrontato il problema della ricostruzione di segnali distribuiti tramite la raccolta di un piccolo numero di campioni al punto di raccolta dati (DCP). Viene sfruttato il metodo dell'analisi delle componenti principali (PCA) per ricostruire al DCP le caratteristiche statistiche del segnale di interesse. Questa informazione viene utilizzata al DCP per determinare la matrice richiesta dalle tecniche di recupero che sfruttano algoritmi di ottimizzazione convessa (Compressive Sensing, CS) per ricostruire l'intero segnale da una sua versione campionata. Per integrare questo modello di monitoraggio in un framework di compressione e recupero del segnale, viene applicata la logica del paradigma 'cognitive': prima si osserva la rete; poi dall'osservazione si derivano le statistiche di interesse, che vengono applicate per il recupero del segnale; si sfruttano queste informazioni statistiche per prenderere decisioni e infine si rendono effettive queste decisioni con un controllo in retroazione. Il framework di compressione e recupero con controllo in retroazione è chiamato "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). L'intero framework è stato implementato in una architettura per WSN detta WSN-control, accessibile da Internet. Le scelte nella progettazione del protocollo sono state giustificate da un'analisi teorica con un approccio di tipo Bayesiano. Nella seconda parte della tesi il paradigma cognitive viene utilizzato per l'ottimizzazione di reti locali wireless (WLAN). L'architetture della rete cognitive viene integrata nello stack protocollare della rete wireless. Nello specifico, vengono utilizzati dei modelli grafici probabilistici per modellare lo stack protocollare: le relazioni probabilistiche tra alcuni parametri di diversi livelli vengono studiate con il modello delle reti Bayesiane (BN). In questo modo, è possibile utilizzare queste informazioni provenienti da diversi livelli per ottimizzare le prestazioni della rete, utilizzando un approccio di tipo cross-layer. Ad esempio, queste informazioni sono utilizzate per predire il throughput a livello di trasporto in una rete wireless di tipo single-hop, o per prevedere il verificarsi di eventi di congestione in una rete wireless di tipo multi-hop. L'approccio seguito nei due argomenti principali che compongono questa tesi è il seguente: (i) viene applicato il paradigma cognitive per ricostruire specifiche caratteristiche probabilistiche della rete, (ii) queste informazioni vengono utilizzate per progettare nuove tecniche protocollari, (iii) queste tecniche vengono analizzate teoricamente e confrontate con altre tecniche esistenti, e (iv) le prestazioni vengono simulate, confrontate con quelle di altre tecniche e valutate in scenari di rete realistici.
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Ramani, Shiva Shankar. "Graphical Probabilistic Switching Model: Inference and Characterization for Power Dissipation in VLSI Circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000497.

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Bortolini, Rafaela. "Enhancing building performance : a Bayesian network model to support facility management." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666187.

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The performance of existing buildings is receiving increased concern due to the need to renovate the aging building stock and provide better quality of life for end users. The conservation state of buildings and the indoor environment conditions have been related to occupants’ well-being, health, and productivity. At the same time, there is a need for more sustainable buildings with reduced energy consumption. Most challenges encountered during the analysis of the performance of existing buildings are associated with the complex relationships among the causal factors involved. The performance of a building is influenced by several factors (e.g., environmental agents, occupant behavior, operation, maintenance), which also generate uncertainties when predicting it. Most previous studies that investigate methods to assess a building’s performance do not consider the uncertainty and are often based on linear models. Although different stakeholders’ requirements regarding building performance coexist, few studies centered on the implications of these requirements. Previous studies tend to be highly specific on indicators related to a particular performance aspect, overlooking potential trade-offs that may occur between them. Therefore, a holistic and integrated approach to manage the performance of existing buildings has not been explored. Facility managers need an efficient approach to deal with uncertainty, to manage risks, and systematically identify, analyze, evaluate and mitigate factors that may impact the building performance. Taking into account the aforementioned aspects, the aim of this thesis is to devise a Bayesian network (BN) model to holistically manage the operational performance of buildings and support facility management. The proposed model consists of an integrated probabilistic approach to assess the performance of existing buildings, considering three categories: safety and elements working properly, health and comfort, and energy efficiency. The model also provides an understanding of the causality chain between multiple factors and indicators regarding building performance. The understanding of the relationships between building condition, end user comfort and building energy efficiency, supports facility managers to unwind a causal explanation for the performance results in a reasoning process. The proposed model is tested and validated using sensitivity analysis and data from existing buildings. A set of model applications are discussed, including the assessment of a building’s performance holistically, the identification of causal factors, the prediction of building performance through renovation and retrofit scenarios, and the prioritization of maintenance actions. Case studies also allow to illustrate the applicability of the model for ensuring that its interactions and outcomes are feasible. Scenario analyses provide a basis for a deeper understanding of the potential responses of the model, helping facility managers to optimize operation strategies of buildings in order to enhance its performance. The results of this thesis also include data collection methods for the inputs of the proposed BN model. A building inspection system is proposed to evaluate the technical performance of buildings, a text-mining approach is developed to analyze maintenance requests of end users, and a questionnaire is formulated to collect end-user satisfaction regarding building comfort. To conclude, this work proposes the use of Building Information Modeling (BIM) to store and access building information, which are typically disperse and not standardized in existing buildings.
Actualmente, el desempeño de los edificios existentes es de gran interés debido a la necesidad de renovar el stock de edificios antiguos, proporcionando así una mejor calidad de vida a los usuarios finales. El estado de conservación de los edificios y las condiciones ambientales interiores se relacionan con el bienestar, la salud y la productividad de los ocupantes. Al mismo tiempo, existe la necesidad de edificios más sostenibles con un menor consumo energético. El desempeño de un edificio se ve afectado por varios factores (p.ej., agentes ambientales, comportamiento de los ocupantes, operación, mantenimiento, etc.). La mayoría de estos aspectos y causas muestran complejas relaciones, y consecuentemente existe una gran incertidumbre para predecirlo. Sin embargo, las investigaciones anteriores no contemplan estas relaciones causales y, a menudo, se basan en modelos lineales. Aunque el desempeño de los edificios se debe abordar teniendo en cuenta los requisitos de las diferentes partes interesadas, pocos estudios se centran en este enfoque. Los estudios anteriores tienden a analizar aspectos particulares del desempeño, ignorando las posibles relaciones que pueden ocurrir entre ellos. Los gestores de edificios deben abordar eficientemente la incertidumbre, gestionar los riesgos e identificar, analizar, evaluar y mitigar sistemáticamente los factores que pueden afectar el desempeño del edificio. Teniendo en cuenta los aspectos comentados anteriormente, el objetivo de esta tesis es desarrollar un modelo de red bayesiana (BN) para gestionar holísticamente el desempeño operativo de los edificios y apoyar su gestión. El modelo propuesto consiste en un enfoque probabilístico para evaluar el desempeño de los edificios existentes, considerando tres categorías: seguridad y funcionalidad, salud y confort, y eficiencia energética. El modelo también proporciona una interpretación de la cadena de causalidad entre los múltiples factores e indicadores relacionados con el desempeño del edificio. El análisis de las relaciones entre los diferentes aspectos del desempeño de los edificios (estado de conservación del edificio, el confort del usuario final y la eficiencia energética del edificio) va a permitir explicar y entender sus factores causales y va a posibilitar mejorar la gestión de estos edificios. La verificación del modelo propuesto se lleva a cabo mediante análisis de sensibilidad y datos de edificios existentes. Las aplicaciones del modelo incluyen: la evaluación del desempeño de edificios de forma integrada; la identificación de factores causales; la predicción del desempeño de los edificios a través de escenarios de renovación y modernización; y la priorización de las acciones de mantenimiento. La implementación del modelo en diversos casos de estudio permite ilustrar su aplicabilidad y validar su uso. Los resultados de esta tesis también incluyen métodos de recogida de datos para las variables del modelo propuesto. De hecho, se propone un sistema de inspección de edificios para evaluar el desempeño técnico de los edificios, se desarrolla un sistema de text mining para analizar las solicitudes de mantenimiento de los usuarios finales y se formula un cuestionario para recoger la satisfacción de los usuarios finales en relación a los espacios de los edificios en los que interactúan. Para concluir, este trabajo propone el uso del Building Information Modeling (BIM) para almacenar y acceder a la información necesaria para el modelo.
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Klukowski, Piotr. "Nuclear magnetic resonance spectroscopy interpretation for protein modeling using computer vision and probabilistic graphical models." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4720.

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Dynamic development of nuclear magnetic resonance spectroscopy (NMR) allowed fast acquisition of experimental data which determine structure and dynamics of macromolecules. Nevertheless, due to lack of appropriate computational methods, NMR spectra are still analyzed manually by researchers what takes weeks or years depending on protein complexity. Therefore automation of this process is extremely desired and can significantly reduce time of protein structure solving. In presented work, a new approach to automated three-dimensional protein NMR spectra analysis is presented. It is based on Histogram of Oriented Gradients and Bayesian Network which have not been ever applied in that context in the history of research in the area. Proposed method was evaluated using benchmark data which was established by manual labeling of 99 spectroscopic images taken from 6 different NMR experiments. Afterwards subsequent validation was made using spectra of upstream of N-ras protein. With the use of proposed method, a three-dimensional structure of mentioned protein was calculated. Comparison with reference structure from protein databank reveals no significant differences what has proven that proposed method can be used in practice in NMR laboratories.
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Ramalingam, Nirmal Munuswamy. "A complete probabilistic framework for learning input models for power and crosstalk estimation in VLSI circuits." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000505.

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Tran, Thanh Binh. "A Bayesian Network framework for probabilistic identification of model parameters from normal and accelerated tests : application to chloride ingress into conrete." Nantes, 2015. https://archive.bu.univ-nantes.fr/pollux/show/show?id=1bd3c7d5-c357-43f1-b430-bb5e97e9ef3c.

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La pénétration des chlorures dans le béton est l'une des causes principales de dégradation des ouvrages en béton armé. Sous l’attaque des chlorures des dégradations importantes auront lieu après 10 à 20 ans. Par conséquent, ces ouvrages devraient être périodiquement inspectés et réparés afin d’assurer des niveaux optimaux de capacité de service et de sécurité pendant leur durée de vie. Des paramètres matériels et environnementaux pertinents peuvent être déterminés à partir des données d’inspection. En raison de la cinétique longue des mécanismes de pénétration de chlorures et des difficultés pour mettre en place des techniques d'inspection, il est difficile d'obtenir des données d'inspection suffisantes pour caractériser le comportement à moyen et à long-terme de ce phénomène. L'objectif principal de cette thèse est de développer une méthodologie basée sur la mise à jour du réseau bayésien pour améliorer l'identification des incertitudes liées aux paramètres matériels et environnementaux des modèles en cas de quantité limitée de mesures. Le processus d'identification est appuyé sur des résultats provenant de tests normaux et accélérées effectués en laboratoire qui simulent les conditions de marée. Sur la base de ces données, plusieurs procédures sont proposées pour : (1) identifier des variables aléatoires d'entrée à partir de tests normaux ou naturels; (2) déterminer un temps équivalent d'exposition (et un facteur d'échelle) pour les tests accélérés; et (3) caractériser les paramètres en dépendants du temps. Les résultats indiquent que le cadre proposé peut être un outil utile pour identifier les paramètres du modèle, même à partir d’une base de données limitée
Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete (RC) structures. Under chloride attack important damages are generated after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety during its lifecycle. Relevant material and environmental parameters for reliability analysis could be determined from inspection data. In natural conditions, chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. However, due to the slow process of chloride ingress and the difficulties for implementing the inspection techniques, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters in case of limited amount of measurements in time and space. The identification process is based on results coming from in-lab normal and accelerated tests that simulate tidal conditions. Based on these data, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine an equivalent exposure time (and a scale factor) for accelerated tests; and (3) characterise time-dependent parameters combining information from normal and accelerated tests. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited
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Books on the topic "Probabilistic Bayesian Network"

1

Lim, Chee Peng. Probabilistic fuzzy ARTMAP: An autonomous neural network architecture for Bayesian probability estimation. Sheffield: University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1995.

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A, Gammerman, and UNICOM Seminars, eds. Probabilistic reasoning and Bayesian belief networks. Henley-on-Thames: Alfred Waller in association with UNICOM, 1995.

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Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470091754.

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Probabilistic methods for bionformatics: With an introduction to Bayesian networks. Burlington, MA: Morgan Kaufmann Publishers, 2009.

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Taroni, Franco, Alex Biedermann, Silvia Bozza, Paolo Garbolino, and Colin Aitken. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118914762.

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1955-, Lucas Peter, Gámez José A, and Salmerón Antonio, eds. Advances in probabilistic graphical models. Berlin: Springer, 2007.

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Taroni, Franco, Colin Aitken, Paolo Garbolino, and Alex Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. Wiley & Sons, Limited, John, 2006.

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Cowell, Robert G., David J. Spiegelhalter, Steffen L. Lauritzen, and Philip Dawid. Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks. Springer London, Limited, 2006.

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Neapolitan, Richard E. Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks. Elsevier Science & Technology Books, 2009.

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Probabilistic Reasoning and Bayesian Belief Networks (UNICOM - Information & Communications Technology). Nelson Thornes Ltd, 1998.

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Book chapters on the topic "Probabilistic Bayesian Network"

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Butz, Cory J., Jhonatan de S. Oliveira, and Anders L. Madsen. "Bayesian Network Inference Using Marginal Trees." In Probabilistic Graphical Models, 81–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_6.

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Castillo, Enrique, José Manuel Gutiérrez, and Ali S. Hadi. "Learning Bayesian Networks." In Expert Systems and Probabilistic Network Models, 481–527. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2270-5_11.

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Land, Walker H., and J. David Schaffer. "Bayesian Probabilistic Neural Network (BPNN)." In The Art and Science of Machine Intelligence, 187–210. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18496-4_7.

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Muller, Alexandre, Marie-Christine Suhner, and Benoît Iung. "Bayesian Network-based Proactive Maintenance." In Probabilistic Safety Assessment and Management, 2066–71. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_332.

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Ben Mrad, Ali, Véronique Delcroix, Sylvain Piechowiak, and Philip Leicester. "From Information to Evidence in a Bayesian Network." In Probabilistic Graphical Models, 33–48. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_3.

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Kraisangka, Jidapa, and Marek J. Druzdzel. "Discrete Bayesian Network Interpretation of the Cox’s Proportional Hazards Model." In Probabilistic Graphical Models, 238–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_16.

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Zhang, Chenjing, Kun Yue, Jinghua Zhu, Xiaoling Wang, and Aoying Zhou. "Bayesian Network-Based Probabilistic XML Keywords Filtering." In Database Systems for Advanced Applications, 274–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29023-7_28.

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Suzuki, Joe. "Learning Bayesian Network Structures When Discrete and Continuous Variables Are Present." In Probabilistic Graphical Models, 471–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_31.

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Zhou, Yun, Norman Fenton, and Martin Neil. "An Extended MPL-C Model for Bayesian Network Parameter Learning with Exterior Constraints." In Probabilistic Graphical Models, 581–96. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11433-0_38.

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Lee, Chang-Ju, and Kun-Jai Lee. "Application of Bayesian Network Considering the Special Dependency to Stochastic Events." In Probabilistic Safety Assessment and Management, 2866–71. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_459.

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Conference papers on the topic "Probabilistic Bayesian Network"

1

Moura, Gabriel, and Mauro Roisenberg. "Probabilistic Fuzzy Bayesian Network." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7381989.

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Goštautaitė, Daiva. "DYNAMIC LEARNING STYLE MODELLING USING PROBABILISTIC BAYESIAN NETWORK." In 11th International Conference on Education and New Learning Technologies. IATED, 2019. http://dx.doi.org/10.21125/edulearn.2019.0781.

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Xu, Jie, Xingyu Chen, Xuguang Lan, and Nanning Zheng. "Probabilistic Human Motion Prediction via A Bayesian Neural Network." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561665.

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Bourgault, Frederic, Nisar Ahmed, Danelle Shah, and Mark Campbell. "Probabilistic Operator-Multiple Robot Modeling Using Bayesian Network Representation." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-6589.

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Shoji, Tomoaki, Wataru Hirohashi, Yu Fujimoto, and Yasuhiro Hayashi. "Home energy management based on Bayesian network considering resident convenience." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960597.

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Gour, Riti, Genya Ishigaki, Jian Kong, and Jason P. Jue. "Localization of Probabilistic Correlated Failures in Virtual Network Infrastructures using Bayesian Networks." In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/ofc.2020.th1f.4.

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Chakroun, Imen, Tom Haber, Tom Vander Aa, and Thomas Kovac. "Exploring Parallel Implementations of the Bayesian Probabilistic Matrix Factorization." In 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2016. http://dx.doi.org/10.1109/pdp.2016.48.

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Kwag, Shinyoung, and Abhinav Gupta. "Bayesian Network Technique in Probabilistic Risk Assessment for Multiple Hazards." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60723.

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Conventional probabilistic risk assessment (PRA) methodologies (USNRC, 1983; IAEA, 1992; EPRI, 1994; Ellingwood, 2001) conduct risk assessment for different external hazards by considering each hazard separately and independent of each other. The risk metric for a specific hazard is evaluated by a convolution of the fragility and the hazard curves. The fragility curve for basic event is obtained by using empirical, experimental, and/or numerical simulation data for a particular hazard. Treating each hazard as an independent mutually exclusive event can be inappropriate in some cases as certain hazards are statistically correlated or dependent. Examples of such correlated events include but are not limited to flooding induced fire, seismically induced internal or external flooding, or even seismically induced fire. In the current practice, system level risk and consequence sequences are typically calculated using a Fault Tree Analysis (FTA) that uses logic gates to express the causative relationship between events. Furthermore, the basic events in an FTA are considered as independent. Therefore, conducting a multi-hazard PRA using a Fault Tree is quite impractical. In some cases using an FTA to conduct a multi-hazard PRA can even be inaccurate because an FTA cannot account for uncertainties in events and the use of logic gates limits the consideration of statistical correlations or dependencies between the events. An additional limitation of an FTA based PRA is embedded in its inability to easily accommodate newly observed data and calculation of updated risk or accident scenarios under the newly available information. Finally, FTA is not best suited for addressing beyond design basis vulnerabilities. Therefore, in this paper, we present the results from a study on multi-hazard risk assessment that is conducted using a Bayesian network (BN) with Bayesian inference. The framework can consider general relationships among risks from multiple hazards, allows updating by considering the newly available data/information at any level, and evaluate scenarios for vulnerabilities due to beyond design bases events.
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See, John. "Probabilistic Bayesian network classifier for face recognition in video sequences." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121770.

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Guan, Xuefei, Ratneshwar Jha, and Yongming Liu. "A probabilistic multi-model Bayesian network for fatigue damage prognosis." In 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-1702.

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Reports on the topic "Probabilistic Bayesian Network"

1

Moler, Edward J., and I. S. Mian. Analysis of molecular expression patterns and integration with other knowledge bases using probabilistic Bayesian network models. Office of Scientific and Technical Information (OSTI), March 2000. http://dx.doi.org/10.2172/753888.

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Groth, Katrina, and Laura Swiler. Use of limited data to construct Bayesian networks for probabilistic risk assessment. Office of Scientific and Technical Information (OSTI), March 2013. http://dx.doi.org/10.2172/1095131.

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Utsugi, Akio, and Motoyuki Akamatsu. Analysis of Car-Following Behavior Using Dynamic Probabilistic Models~Identification of Driving Mode Transition Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0241.

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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), June 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapolating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.
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