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You searched for subject:(Dynamic Bayesian Networks). Showing records 1 – 30 of 40 total matches.

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Delft University of Technology

1. Seckington, M.J. Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:.

Degree: 2011, Delft University of Technology

 Automatic analysis of facial expressions is a complex area of pattern recognition and computer vision with many un- resolved problems, one of which is the… (more)

Subjects/Keywords: dynamic bayesian networks; facial expression recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Seckington, M. J. (2011). Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:17de3262-b7a3-400b-8fbc-1b0a25d4d957

Chicago Manual of Style (16th Edition):

Seckington, M J. “Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:.” 2011. Masters Thesis, Delft University of Technology. Accessed December 11, 2019. http://resolver.tudelft.nl/uuid:17de3262-b7a3-400b-8fbc-1b0a25d4d957.

MLA Handbook (7th Edition):

Seckington, M J. “Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:.” 2011. Web. 11 Dec 2019.

Vancouver:

Seckington MJ. Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2019 Dec 11]. Available from: http://resolver.tudelft.nl/uuid:17de3262-b7a3-400b-8fbc-1b0a25d4d957.

Council of Science Editors:

Seckington MJ. Using Dynamic Bayesian Networks for Posed versus Spontaneous Facial Expression Recognition:. [Masters Thesis]. Delft University of Technology; 2011. Available from: http://resolver.tudelft.nl/uuid:17de3262-b7a3-400b-8fbc-1b0a25d4d957


IUPUI

2. Kilaru, Gokhul Krishna. USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION.

Degree: 2012, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

Bayesian networks, one of the most widely used techniques to understand or predict the future by making use of current… (more)

Subjects/Keywords: BAYESIAN NETWORKS, GENE EXPRESSION, DYNAMIC BAYESIAN NETWORKS, HIDDEN MARKOV MODELS; Bayesian statistical decision theory; Gene expression

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APA (6th Edition):

Kilaru, G. K. (2012). USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/2774

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kilaru, Gokhul Krishna. “USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION.” 2012. Thesis, IUPUI. Accessed December 11, 2019. http://hdl.handle.net/1805/2774.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kilaru, Gokhul Krishna. “USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION.” 2012. Web. 11 Dec 2019.

Vancouver:

Kilaru GK. USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION. [Internet] [Thesis]. IUPUI; 2012. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1805/2774.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kilaru GK. USE OF APRIORI KNOWLEDGE ON DYNAMIC BAYESIAN MODELS IN TIME-COURSE EXPRESSION DATA PREDICTION. [Thesis]. IUPUI; 2012. Available from: http://hdl.handle.net/1805/2774

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

3. Bandyopadhyay, Sunayan. Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models.

Degree: PhD, Computer Science, 2016, University of Minnesota

 Cardiovascular (CV) disease is one of the leading causes of death in the United States; therefore, it is of vital importance that it be managed… (more)

Subjects/Keywords: Bayesian networks; Cardiovascular risk model; dynamic Bayesian network; Electronic health record data; structure learning

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APA (6th Edition):

Bandyopadhyay, S. (2016). Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182297

Chicago Manual of Style (16th Edition):

Bandyopadhyay, Sunayan. “Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models.” 2016. Doctoral Dissertation, University of Minnesota. Accessed December 11, 2019. http://hdl.handle.net/11299/182297.

MLA Handbook (7th Edition):

Bandyopadhyay, Sunayan. “Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models.” 2016. Web. 11 Dec 2019.

Vancouver:

Bandyopadhyay S. Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/11299/182297.

Council of Science Editors:

Bandyopadhyay S. Cardiovascular risk prediction from Electronic Health Records using probabilistic graphical models. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182297


Brunel University

4. Kiourktsidis, Ilias. Flexible cross layer design for improved quality of service in MANETs.

Degree: 2011, Brunel University

 Mobile Ad hoc Networks (MANETs) are becoming increasingly important because of their unique characteristics of connectivity. Several delay sensitive applications are starting to appear in… (more)

Subjects/Keywords: 004.6; Genetic algorithm; Bayesian networks; Machine learning; Dynamic routing; Flow control

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APA (6th Edition):

Kiourktsidis, I. (2011). Flexible cross layer design for improved quality of service in MANETs. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/7464 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575654

Chicago Manual of Style (16th Edition):

Kiourktsidis, Ilias. “Flexible cross layer design for improved quality of service in MANETs.” 2011. Doctoral Dissertation, Brunel University. Accessed December 11, 2019. http://bura.brunel.ac.uk/handle/2438/7464 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575654.

MLA Handbook (7th Edition):

Kiourktsidis, Ilias. “Flexible cross layer design for improved quality of service in MANETs.” 2011. Web. 11 Dec 2019.

Vancouver:

Kiourktsidis I. Flexible cross layer design for improved quality of service in MANETs. [Internet] [Doctoral dissertation]. Brunel University; 2011. [cited 2019 Dec 11]. Available from: http://bura.brunel.ac.uk/handle/2438/7464 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575654.

Council of Science Editors:

Kiourktsidis I. Flexible cross layer design for improved quality of service in MANETs. [Doctoral Dissertation]. Brunel University; 2011. Available from: http://bura.brunel.ac.uk/handle/2438/7464 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.575654


Virginia Tech

5. Patnaik, Debprakash. Multiple Uses of Frequent Episodes in Temporal Process Modeling.

Degree: PhD, Computer Science, 2011, Virginia Tech

 This dissertation investigates algorithmic techniques for temporal process discovery in many domains. Many different formalisms have been proposed for modeling temporal processes such as motifs,… (more)

Subjects/Keywords: motifs; graphical models; frequent episodes; dynamic Bayesian networks; temporal data mining

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APA (6th Edition):

Patnaik, D. (2011). Multiple Uses of Frequent Episodes in Temporal Process Modeling. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28413

Chicago Manual of Style (16th Edition):

Patnaik, Debprakash. “Multiple Uses of Frequent Episodes in Temporal Process Modeling.” 2011. Doctoral Dissertation, Virginia Tech. Accessed December 11, 2019. http://hdl.handle.net/10919/28413.

MLA Handbook (7th Edition):

Patnaik, Debprakash. “Multiple Uses of Frequent Episodes in Temporal Process Modeling.” 2011. Web. 11 Dec 2019.

Vancouver:

Patnaik D. Multiple Uses of Frequent Episodes in Temporal Process Modeling. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10919/28413.

Council of Science Editors:

Patnaik D. Multiple Uses of Frequent Episodes in Temporal Process Modeling. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/28413


University of Waterloo

6. Smart, Michael. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.

Degree: 2016, University of Waterloo

 Lane detection is a fundamental and challenging task in autonomous driving and must be performed safely and robustly to avoid catastrophic failures. Current methods do… (more)

Subjects/Keywords: Autonomous Driving; Dynamic Bayesian Networks; Lane Detection; Semi-supervised Machine Learning

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APA (6th Edition):

Smart, M. (2016). Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10454

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Smart, Michael. “Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.” 2016. Thesis, University of Waterloo. Accessed December 11, 2019. http://hdl.handle.net/10012/10454.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Smart, Michael. “Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.” 2016. Web. 11 Dec 2019.

Vancouver:

Smart M. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10012/10454.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Smart M. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10454

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

7. Melnyk, Igor. Dynamic Bayesian Networks: Estimation, Inference and Applications.

Degree: PhD, Computer Science, 2016, University of Minnesota

 In recent years, there has been a significant increase in the applications dealing with dynamic, high-dimensional, heterogeneous data streams. For example, in the domains such… (more)

Subjects/Keywords: Anomaly Detection; Dynamic Bayesian Networks; Time Series Analysis

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APA (6th Edition):

Melnyk, I. (2016). Dynamic Bayesian Networks: Estimation, Inference and Applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182162

Chicago Manual of Style (16th Edition):

Melnyk, Igor. “Dynamic Bayesian Networks: Estimation, Inference and Applications.” 2016. Doctoral Dissertation, University of Minnesota. Accessed December 11, 2019. http://hdl.handle.net/11299/182162.

MLA Handbook (7th Edition):

Melnyk, Igor. “Dynamic Bayesian Networks: Estimation, Inference and Applications.” 2016. Web. 11 Dec 2019.

Vancouver:

Melnyk I. Dynamic Bayesian Networks: Estimation, Inference and Applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/11299/182162.

Council of Science Editors:

Melnyk I. Dynamic Bayesian Networks: Estimation, Inference and Applications. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182162


Queen Mary, University of London

8. Ogunsanya, Oluwole Victor. Decision support using Bayesian networks for clinical decision making.

Degree: PhD, 2012, Queen Mary, University of London

 This thesis investigates the use of Bayesian Networks (BNs), augmented by the Dynamic Discretization Algorithm, to model a variety of clinical problems. In particular, the… (more)

Subjects/Keywords: 616.07540285; Computer Science; Bayesian Networks; Dynamic Discretization Algorithm

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APA (6th Edition):

Ogunsanya, O. V. (2012). Decision support using Bayesian networks for clinical decision making. (Doctoral Dissertation). Queen Mary, University of London. Retrieved from http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555115

Chicago Manual of Style (16th Edition):

Ogunsanya, Oluwole Victor. “Decision support using Bayesian networks for clinical decision making.” 2012. Doctoral Dissertation, Queen Mary, University of London. Accessed December 11, 2019. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555115.

MLA Handbook (7th Edition):

Ogunsanya, Oluwole Victor. “Decision support using Bayesian networks for clinical decision making.” 2012. Web. 11 Dec 2019.

Vancouver:

Ogunsanya OV. Decision support using Bayesian networks for clinical decision making. [Internet] [Doctoral dissertation]. Queen Mary, University of London; 2012. [cited 2019 Dec 11]. Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555115.

Council of Science Editors:

Ogunsanya OV. Decision support using Bayesian networks for clinical decision making. [Doctoral Dissertation]. Queen Mary, University of London; 2012. Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/8688 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555115


University of California – San Diego

9. Li, Weixin. Visual Understanding of Complex Human Behavior via Attribute Dynamics.

Degree: Electrical Engineering (Signal and Image Proc), 2016, University of California – San Diego

 Visual understanding of human behavior in video sequences is one of the fundamental topics in computational vision. Being a sequential signal by nature, most critical… (more)

Subjects/Keywords: Computer science; Action Recognition; Artificial Intelligence; Computer Vision; Dynamic Bayesian Networks; Machine Learning; Variational Inference

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APA (6th Edition):

Li, W. (2016). Visual Understanding of Complex Human Behavior via Attribute Dynamics. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/0sm5h465

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Weixin. “Visual Understanding of Complex Human Behavior via Attribute Dynamics.” 2016. Thesis, University of California – San Diego. Accessed December 11, 2019. http://www.escholarship.org/uc/item/0sm5h465.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Weixin. “Visual Understanding of Complex Human Behavior via Attribute Dynamics.” 2016. Web. 11 Dec 2019.

Vancouver:

Li W. Visual Understanding of Complex Human Behavior via Attribute Dynamics. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Dec 11]. Available from: http://www.escholarship.org/uc/item/0sm5h465.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li W. Visual Understanding of Complex Human Behavior via Attribute Dynamics. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/0sm5h465

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Queensland University of Technology

10. Wang, Ruizi. Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS).

Degree: 2012, Queensland University of Technology

 The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure… (more)

Subjects/Keywords: health prediction; dynamic object oriented Bayesian networks (DOOBNS); bridge deterioration model; condition states; structural reliability

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APA (6th Edition):

Wang, R. (2012). Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS). (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/53204/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Ruizi. “Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS).” 2012. Thesis, Queensland University of Technology. Accessed December 11, 2019. https://eprints.qut.edu.au/53204/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Ruizi. “Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS).” 2012. Web. 11 Dec 2019.

Vancouver:

Wang R. Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS). [Internet] [Thesis]. Queensland University of Technology; 2012. [cited 2019 Dec 11]. Available from: https://eprints.qut.edu.au/53204/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang R. Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS). [Thesis]. Queensland University of Technology; 2012. Available from: https://eprints.qut.edu.au/53204/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Queen Mary, University of London

11. Biresaw, Tewodros Atanaw. Self-correcting Bayesian target tracking.

Degree: PhD, 2015, Queen Mary, University of London

 Visual tracking, a building block for many applications, has challenges such as occlusions,illumination changes, background clutter and variable motion dynamics that may degrade the tracking… (more)

Subjects/Keywords: 629.8; visual tracking; Track-Evaluate- Correct framework; Dynamic Bayesian Networks; self-correcting tracking.

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APA (6th Edition):

Biresaw, T. A. (2015). Self-correcting Bayesian target tracking. (Doctoral Dissertation). Queen Mary, University of London. Retrieved from http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658668

Chicago Manual of Style (16th Edition):

Biresaw, Tewodros Atanaw. “Self-correcting Bayesian target tracking.” 2015. Doctoral Dissertation, Queen Mary, University of London. Accessed December 11, 2019. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658668.

MLA Handbook (7th Edition):

Biresaw, Tewodros Atanaw. “Self-correcting Bayesian target tracking.” 2015. Web. 11 Dec 2019.

Vancouver:

Biresaw TA. Self-correcting Bayesian target tracking. [Internet] [Doctoral dissertation]. Queen Mary, University of London; 2015. [cited 2019 Dec 11]. Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658668.

Council of Science Editors:

Biresaw TA. Self-correcting Bayesian target tracking. [Doctoral Dissertation]. Queen Mary, University of London; 2015. Available from: http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658668


George Mason University

12. Martin, Todd William. Probabilistic Reasoning for Dynamic Spectrum Access .

Degree: 2016, George Mason University

Dynamic Spectrum Access (DSA) systems combine situational awareness development, decision assessment, and spectrum adaptation to provide greater spectrum access to wireless systems. While significant progress… (more)

Subjects/Keywords: Engineering; Systems science; Computer science; Bayesian Networks; Dynamic Spectrum Access; Probabilistic Reasoning; Wireless Communications

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APA (6th Edition):

Martin, T. W. (2016). Probabilistic Reasoning for Dynamic Spectrum Access . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/10630

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Martin, Todd William. “Probabilistic Reasoning for Dynamic Spectrum Access .” 2016. Thesis, George Mason University. Accessed December 11, 2019. http://hdl.handle.net/1920/10630.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Martin, Todd William. “Probabilistic Reasoning for Dynamic Spectrum Access .” 2016. Web. 11 Dec 2019.

Vancouver:

Martin TW. Probabilistic Reasoning for Dynamic Spectrum Access . [Internet] [Thesis]. George Mason University; 2016. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1920/10630.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Martin TW. Probabilistic Reasoning for Dynamic Spectrum Access . [Thesis]. George Mason University; 2016. Available from: http://hdl.handle.net/1920/10630

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

13. Pichené, Matthieu. Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment.

Degree: Docteur es, Informatique, 2018, Rennes 1

Cette thèse examine une nouvelle façon d'étudier l'impact d'une voie de signalisation donnée sur l'évolution d'un tissu grâce à l'analyse multi-niveaux. Cette analyse est divisée… (more)

Subjects/Keywords: Biologie computationnelle; Modèles stochastiques; Réseaux Bayésiens Dynamiques; Computationnal Biology; Stochastic Models; Dynamic Bayesian Networks

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APA (6th Edition):

Pichené, M. (2018). Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment. (Doctoral Dissertation). Rennes 1. Retrieved from http://www.theses.fr/2018REN1S026

Chicago Manual of Style (16th Edition):

Pichené, Matthieu. “Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment.” 2018. Doctoral Dissertation, Rennes 1. Accessed December 11, 2019. http://www.theses.fr/2018REN1S026.

MLA Handbook (7th Edition):

Pichené, Matthieu. “Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment.” 2018. Web. 11 Dec 2019.

Vancouver:

Pichené M. Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment. [Internet] [Doctoral dissertation]. Rennes 1; 2018. [cited 2019 Dec 11]. Available from: http://www.theses.fr/2018REN1S026.

Council of Science Editors:

Pichené M. Analyse multi-niveaux en biologie systémique computationnelle : le cas des cellules HeLa sous traitement apoptotique : Multi-level analysis in computational system biology : the case of HeLa cells under apoptosis treatment. [Doctoral Dissertation]. Rennes 1; 2018. Available from: http://www.theses.fr/2018REN1S026


Arizona State University

14. Reichenberg, Raymond E. The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables.

Degree: Family and Human Development, 2018, Arizona State University

Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present… (more)

Subjects/Keywords: Educational tests &; measurements; Educational psychology; Quantitative psychology; dynamic Bayesian networks; educational measurement; game-based assessment

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APA (6th Edition):

Reichenberg, R. E. (2018). The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/50531

Chicago Manual of Style (16th Edition):

Reichenberg, Raymond E. “The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables.” 2018. Doctoral Dissertation, Arizona State University. Accessed December 11, 2019. http://repository.asu.edu/items/50531.

MLA Handbook (7th Edition):

Reichenberg, Raymond E. “The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables.” 2018. Web. 11 Dec 2019.

Vancouver:

Reichenberg RE. The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables. [Internet] [Doctoral dissertation]. Arizona State University; 2018. [cited 2019 Dec 11]. Available from: http://repository.asu.edu/items/50531.

Council of Science Editors:

Reichenberg RE. The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables. [Doctoral Dissertation]. Arizona State University; 2018. Available from: http://repository.asu.edu/items/50531


University of Edinburgh

15. Dielmann, Alfred. Automatic recognition of multiparty human interactions using dynamic Bayesian networks.

Degree: 2009, University of Edinburgh

 Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic… (more)

Subjects/Keywords: 006.3; dynamic Bayesian networks; DBN; FLM; factored language models; multimodal; meetings; social signal processing; dialogue acts; meeting actions; machine learning

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APA (6th Edition):

Dielmann, A. (2009). Automatic recognition of multiparty human interactions using dynamic Bayesian networks. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/4022

Chicago Manual of Style (16th Edition):

Dielmann, Alfred. “Automatic recognition of multiparty human interactions using dynamic Bayesian networks.” 2009. Doctoral Dissertation, University of Edinburgh. Accessed December 11, 2019. http://hdl.handle.net/1842/4022.

MLA Handbook (7th Edition):

Dielmann, Alfred. “Automatic recognition of multiparty human interactions using dynamic Bayesian networks.” 2009. Web. 11 Dec 2019.

Vancouver:

Dielmann A. Automatic recognition of multiparty human interactions using dynamic Bayesian networks. [Internet] [Doctoral dissertation]. University of Edinburgh; 2009. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1842/4022.

Council of Science Editors:

Dielmann A. Automatic recognition of multiparty human interactions using dynamic Bayesian networks. [Doctoral Dissertation]. University of Edinburgh; 2009. Available from: http://hdl.handle.net/1842/4022


University of Washington

16. Halloran, John Timothy. Graphical Models for Peptide Identification of Tandem Mass Spectra.

Degree: PhD, 2016, University of Washington

 Graphical models (GMs) provide a flexible framework for modeling phenomena. In the past few decades, GMs have become indispensable tools for machine learning and computational… (more)

Subjects/Keywords: Dynamic Bayesian networks; Graphical models; Machine learning; Peptide identification; Proteomics; Tandem mass spectrometry; Bioinformatics; Computer science; Biostatistics; electrical engineering

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APA (6th Edition):

Halloran, J. T. (2016). Graphical Models for Peptide Identification of Tandem Mass Spectra. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/35573

Chicago Manual of Style (16th Edition):

Halloran, John Timothy. “Graphical Models for Peptide Identification of Tandem Mass Spectra.” 2016. Doctoral Dissertation, University of Washington. Accessed December 11, 2019. http://hdl.handle.net/1773/35573.

MLA Handbook (7th Edition):

Halloran, John Timothy. “Graphical Models for Peptide Identification of Tandem Mass Spectra.” 2016. Web. 11 Dec 2019.

Vancouver:

Halloran JT. Graphical Models for Peptide Identification of Tandem Mass Spectra. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1773/35573.

Council of Science Editors:

Halloran JT. Graphical Models for Peptide Identification of Tandem Mass Spectra. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/35573


University of Arizona

17. Yan, Ping. SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING .

Degree: 2010, University of Arizona

 RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a… (more)

Subjects/Keywords: Consumer In-store Shopping Behavior; Dynamic Bayesian Networks; Location-aware Marketing; Radio Frequency Identification; Spatial Data Mining

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APA (6th Edition):

Yan, P. (2010). SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/195232

Chicago Manual of Style (16th Edition):

Yan, Ping. “SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING .” 2010. Doctoral Dissertation, University of Arizona. Accessed December 11, 2019. http://hdl.handle.net/10150/195232.

MLA Handbook (7th Edition):

Yan, Ping. “SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING .” 2010. Web. 11 Dec 2019.

Vancouver:

Yan P. SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING . [Internet] [Doctoral dissertation]. University of Arizona; 2010. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/10150/195232.

Council of Science Editors:

Yan P. SPATIAL-TEMPORAL DATA ANALYTICS AND CONSUMER SHOPPING BEHAVIOR MODELING . [Doctoral Dissertation]. University of Arizona; 2010. Available from: http://hdl.handle.net/10150/195232


Delft University of Technology

18. Wiggers, P. Modelling context in automatic speech recognition.

Degree: 2008, Delft University of Technology

 Speech is at the core of human communication. Speaking and listing comes so natural to us that we do not have to think about it… (more)

Subjects/Keywords: automatic speech recognition; language modelling; dynamic Bayesian networks

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APA (6th Edition):

Wiggers, P. (2008). Modelling context in automatic speech recognition. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883

Chicago Manual of Style (16th Edition):

Wiggers, P. “Modelling context in automatic speech recognition.” 2008. Doctoral Dissertation, Delft University of Technology. Accessed December 11, 2019. http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883.

MLA Handbook (7th Edition):

Wiggers, P. “Modelling context in automatic speech recognition.” 2008. Web. 11 Dec 2019.

Vancouver:

Wiggers P. Modelling context in automatic speech recognition. [Internet] [Doctoral dissertation]. Delft University of Technology; 2008. [cited 2019 Dec 11]. Available from: http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883.

Council of Science Editors:

Wiggers P. Modelling context in automatic speech recognition. [Doctoral Dissertation]. Delft University of Technology; 2008. Available from: http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; urn:NBN:nl:ui:24-uuid:78bf003b-e784-40ca-888c-48e0246b3883 ; http://resolver.tudelft.nl/uuid:78bf003b-e784-40ca-888c-48e0246b3883


University of Oulu

19. Schaberreiter, T. (Thomas). A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures.

Degree: 2013, University of Oulu

Abstract Critical Infrastructures (CIs) are an integral part of our society and economy. Services like electricity supply or telecommunication services are expected to be available… (more)

Subjects/Keywords: Bayesian networks; critical infrastructures; dependency; dynamic Bayesian networks; interdependency; modelling; monitoring; risk estimation; risk prediction; simulation; Bayes-verkot; dynaamiset Bayesin verkot; kriittinen infrastruktuuri; mallinnus; monitorointi; riippuvuus; riskiarviointi; riskin ennustaminen; simulointi; sisäinen riippuvuus; Abhängigkeiten; Bayessche Netzwerke; Modellierung; Risikoabschätzung; Risikovorhersage; Simulation; dynamische Bayessche Netzwerke; gegenseitige Abhängigkeiten; kritische Infrastrukturen; Überwachung

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APA (6th Edition):

Schaberreiter, T. (. (2013). A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526202129

Chicago Manual of Style (16th Edition):

Schaberreiter, T (Thomas). “A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures.” 2013. Doctoral Dissertation, University of Oulu. Accessed December 11, 2019. http://urn.fi/urn:isbn:9789526202129.

MLA Handbook (7th Edition):

Schaberreiter, T (Thomas). “A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures.” 2013. Web. 11 Dec 2019.

Vancouver:

Schaberreiter T(. A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures. [Internet] [Doctoral dissertation]. University of Oulu; 2013. [cited 2019 Dec 11]. Available from: http://urn.fi/urn:isbn:9789526202129.

Council of Science Editors:

Schaberreiter T(. A Bayesian network based on-line risk prediction framework for interdependent critical infrastructures. [Doctoral Dissertation]. University of Oulu; 2013. Available from: http://urn.fi/urn:isbn:9789526202129

20. Bouejla, Amal. Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields.

Degree: Docteur es, Sciences et génie des activités à risques, 2014, Paris, ENMP

Ces dernières années, les attaques de pirates contre des navires ou des champs pétroliers n'ont cessé de se multiplier et de s'aggraver. Pour exemple, l'attaque… (more)

Subjects/Keywords: Management des risques; Piraterie maritime; Production offshore; Systèmes d'aide à la décision; Réseaux bayésiens; Réseaux bayésiens dynamiques; Risk management; Maritime piracy; Offshore production; Decision support systems; Bayesian networks; Dynamic bayesian networks; 620

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APA (6th Edition):

Bouejla, A. (2014). Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields. (Doctoral Dissertation). Paris, ENMP. Retrieved from http://www.theses.fr/2014ENMP0076

Chicago Manual of Style (16th Edition):

Bouejla, Amal. “Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields.” 2014. Doctoral Dissertation, Paris, ENMP. Accessed December 11, 2019. http://www.theses.fr/2014ENMP0076.

MLA Handbook (7th Edition):

Bouejla, Amal. “Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields.” 2014. Web. 11 Dec 2019.

Vancouver:

Bouejla A. Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields. [Internet] [Doctoral dissertation]. Paris, ENMP; 2014. [cited 2019 Dec 11]. Available from: http://www.theses.fr/2014ENMP0076.

Council of Science Editors:

Bouejla A. Apports des réseaux bayésiens à la prévention du risque de piraterie à l'encontre des plateformes pétrolières : Contribution of Bayesian networks to the prevention of the risk of piracy against Oil Offshore Fields. [Doctoral Dissertation]. Paris, ENMP; 2014. Available from: http://www.theses.fr/2014ENMP0076


Macquarie University

21. Shermin, Akther. Reconstruction of gene regulatory networks using biological domain knowledge.

Degree: 2012, Macquarie University

"A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy"

"November 2012"

Bibliography: pages173-188.

1. Introduction  – 2. Eukaryotic gene… (more)

Subjects/Keywords: Gene regulatory networks; Bioinformatics; Computational biology; gene regulatory network; dynamic Bayesian network; microarray gene expression data; protein protein interaction data; transcription factor binding site data; yeast cell cycle

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APA (6th Edition):

Shermin, A. (2012). Reconstruction of gene regulatory networks using biological domain knowledge. (Doctoral Dissertation). Macquarie University. Retrieved from http://hdl.handle.net/1959.14/229889

Chicago Manual of Style (16th Edition):

Shermin, Akther. “Reconstruction of gene regulatory networks using biological domain knowledge.” 2012. Doctoral Dissertation, Macquarie University. Accessed December 11, 2019. http://hdl.handle.net/1959.14/229889.

MLA Handbook (7th Edition):

Shermin, Akther. “Reconstruction of gene regulatory networks using biological domain knowledge.” 2012. Web. 11 Dec 2019.

Vancouver:

Shermin A. Reconstruction of gene regulatory networks using biological domain knowledge. [Internet] [Doctoral dissertation]. Macquarie University; 2012. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1959.14/229889.

Council of Science Editors:

Shermin A. Reconstruction of gene regulatory networks using biological domain knowledge. [Doctoral Dissertation]. Macquarie University; 2012. Available from: http://hdl.handle.net/1959.14/229889

22. Meurs, Marie-Jean. Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems.

Degree: Docteur es, Informatique, 2009, Avignon

Les systèmes de dialogue homme-machine ont pour objectif de permettre un échange oral efficace et convivial entre un utilisateur humain et un ordinateur. Leurs domaines… (more)

Subjects/Keywords: Système de dialogue oral; Compréhension du langage parlé; Frame sémantique; Composition sémantique; Réseau bayésien dynamique; Spoken dialog system; Spoken language understanding; Semantic frame; Semantic composition; Dynamic bayesian networks

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APA (6th Edition):

Meurs, M. (2009). Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems. (Doctoral Dissertation). Avignon. Retrieved from http://www.theses.fr/2009AVIG0177

Chicago Manual of Style (16th Edition):

Meurs, Marie-Jean. “Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems.” 2009. Doctoral Dissertation, Avignon. Accessed December 11, 2019. http://www.theses.fr/2009AVIG0177.

MLA Handbook (7th Edition):

Meurs, Marie-Jean. “Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems.” 2009. Web. 11 Dec 2019.

Vancouver:

Meurs M. Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems. [Internet] [Doctoral dissertation]. Avignon; 2009. [cited 2019 Dec 11]. Available from: http://www.theses.fr/2009AVIG0177.

Council of Science Editors:

Meurs M. Approche stochastique bayésienne de la composition sémantique pour les modules de compréhension automatique de la parole dans les systèmes de dialogue homme-machine : A Bayesian Approach of Semantic Composition for Spoken Language Understanding Modules in Spoken Dialog Systems. [Doctoral Dissertation]. Avignon; 2009. Available from: http://www.theses.fr/2009AVIG0177


Delft University of Technology

23. Van den Haak, W.P. Modeling Traffic Information using Bayesian Networks:.

Degree: 2010, Delft University of Technology

 Dutch freeways suffer from severe congestion during rush hours or incidents. Research shows that 64% of congested traffic during rush hour consists of commuter traffic… (more)

Subjects/Keywords: Bayesian Networks; dynamic Routing; travel time estimation; prediction; vehicle speed; forecasting; historical traffic models; traffic information system; freeway; highway; Inductive Loop Data; MONICA data

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APA (6th Edition):

Van den Haak, W. P. (2010). Modeling Traffic Information using Bayesian Networks:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:08ac4987-94f1-4674-9eb3-ede71f491af2

Chicago Manual of Style (16th Edition):

Van den Haak, W P. “Modeling Traffic Information using Bayesian Networks:.” 2010. Masters Thesis, Delft University of Technology. Accessed December 11, 2019. http://resolver.tudelft.nl/uuid:08ac4987-94f1-4674-9eb3-ede71f491af2.

MLA Handbook (7th Edition):

Van den Haak, W P. “Modeling Traffic Information using Bayesian Networks:.” 2010. Web. 11 Dec 2019.

Vancouver:

Van den Haak WP. Modeling Traffic Information using Bayesian Networks:. [Internet] [Masters thesis]. Delft University of Technology; 2010. [cited 2019 Dec 11]. Available from: http://resolver.tudelft.nl/uuid:08ac4987-94f1-4674-9eb3-ede71f491af2.

Council of Science Editors:

Van den Haak WP. Modeling Traffic Information using Bayesian Networks:. [Masters Thesis]. Delft University of Technology; 2010. Available from: http://resolver.tudelft.nl/uuid:08ac4987-94f1-4674-9eb3-ede71f491af2

24. Roychoudhury, Indranil. Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis.

Degree: PhD, Computer Science, 2009, Vanderbilt University

  Early detection and isolation of faults is crucial for ensuring system safety and efficiency. Online diagnosis schemes are usually integrated with fault adaptive control… (more)

Subjects/Keywords: Distributed diagnosis; Continuous systems; Incipient faults; Abrupt faults; Dynamic Bayesian Networks

Page 1 Page 2 Page 3 Page 4 Page 5

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APA (6th Edition):

Roychoudhury, I. (2009). Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-07242009-174724/ ;

Chicago Manual of Style (16th Edition):

Roychoudhury, Indranil. “Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis.” 2009. Doctoral Dissertation, Vanderbilt University. Accessed December 11, 2019. http://etd.library.vanderbilt.edu/available/etd-07242009-174724/ ;.

MLA Handbook (7th Edition):

Roychoudhury, Indranil. “Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis.” 2009. Web. 11 Dec 2019.

Vancouver:

Roychoudhury I. Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis. [Internet] [Doctoral dissertation]. Vanderbilt University; 2009. [cited 2019 Dec 11]. Available from: http://etd.library.vanderbilt.edu/available/etd-07242009-174724/ ;.

Council of Science Editors:

Roychoudhury I. Distributed Diagnosis of Continuous Systems: Global Diagnosis Through Local Analysis. [Doctoral Dissertation]. Vanderbilt University; 2009. Available from: http://etd.library.vanderbilt.edu/available/etd-07242009-174724/ ;


INP Toulouse

25. Liu, Quan. Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management.

Degree: Docteur es, Génie industriel, 2018, INP Toulouse

Le travail aborde la modélisation et l’analyse de systèmes complexes, caractérisés, dans un cadre incertain et évolutif, par de nombreuses interactions entre composants. Dans un… (more)

Subjects/Keywords: Réseaux bayésiens multidimensionnels; Evaluation dynamique; Orienté objet; Modélisation de système complexe; Gestion des risques; Multidimensional Bayesian networks; Dynamic assessment; Object-oriented; Complex system modelling; Risk management; 670

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APA (6th Edition):

Liu, Q. (2018). Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2018INPT0038

Chicago Manual of Style (16th Edition):

Liu, Quan. “Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management.” 2018. Doctoral Dissertation, INP Toulouse. Accessed December 11, 2019. http://www.theses.fr/2018INPT0038.

MLA Handbook (7th Edition):

Liu, Quan. “Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management.” 2018. Web. 11 Dec 2019.

Vancouver:

Liu Q. Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management. [Internet] [Doctoral dissertation]. INP Toulouse; 2018. [cited 2019 Dec 11]. Available from: http://www.theses.fr/2018INPT0038.

Council of Science Editors:

Liu Q. Modélisation bayésienne des interactions multidimensionnelles dans un système complexe : application à la gestion des risques de crues : Bayesian modelling of multidimensional interactions in a complex system : application to flood risk management. [Doctoral Dissertation]. INP Toulouse; 2018. Available from: http://www.theses.fr/2018INPT0038


Uppsala University

26. Jackson, Zara. Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models.

Degree: Statistics, 2019, Uppsala University

  Obesity is a growing problem globally. Currently 2.3 billion adults are overweight, and this number is rising. The most common method for weight loss… (more)

Subjects/Keywords: Basal Metabolic Rate; Resting Metabolic Rate; Dynamic Bayesian Networks; Temporal Models; Food Tracking; Calories; Obesity; Pymc3; Probabilistic Programming; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA (6th Edition):

Jackson, Z. (2019). Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384629

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jackson, Zara. “Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models.” 2019. Thesis, Uppsala University. Accessed December 11, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384629.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jackson, Zara. “Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models.” 2019. Web. 11 Dec 2019.

Vancouver:

Jackson Z. Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models. [Internet] [Thesis]. Uppsala University; 2019. [cited 2019 Dec 11]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384629.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jackson Z. Basal Metabolic Rate (BMR) estimation using Probabilistic Graphical Models. [Thesis]. Uppsala University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384629

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

27. Bolderheij, F. Mission-Driven sensor management analysis, design, implementation and simulation.

Degree: 2007, Delft University of Technology

 The management of sensors onboard of the vessels operated by the Royal Netherlands Navy is becoming increasingly knowledge intensive due to the fact that these… (more)

Subjects/Keywords: sensor management; situational awareness; threat assessment; risk estimation; (Dynamic) Bayesian Networks.

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APA (6th Edition):

Bolderheij, F. (2007). Mission-Driven sensor management analysis, design, implementation and simulation. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae

Chicago Manual of Style (16th Edition):

Bolderheij, F. “Mission-Driven sensor management analysis, design, implementation and simulation.” 2007. Doctoral Dissertation, Delft University of Technology. Accessed December 11, 2019. http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae.

MLA Handbook (7th Edition):

Bolderheij, F. “Mission-Driven sensor management analysis, design, implementation and simulation.” 2007. Web. 11 Dec 2019.

Vancouver:

Bolderheij F. Mission-Driven sensor management analysis, design, implementation and simulation. [Internet] [Doctoral dissertation]. Delft University of Technology; 2007. [cited 2019 Dec 11]. Available from: http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae.

Council of Science Editors:

Bolderheij F. Mission-Driven sensor management analysis, design, implementation and simulation. [Doctoral Dissertation]. Delft University of Technology; 2007. Available from: http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; urn:NBN:nl:ui:24-uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae ; http://resolver.tudelft.nl/uuid:3c23309a-5004-41e2-a2a9-fb1ab1a840ae

28. Evers, S. Sensor Data Management with Probabilistic Models.

Degree: Faculty of Electrical Engineering, Mathematics & Computer Science, 2009, University of Twente

 The anticipated 'sensing environments' of the near future pose new requirements to the data management systems that mediate between sensor data supply and demand sides.… (more)

Subjects/Keywords: METIS-265241; EWI-16525; Sensor data; Probabilistic models; Dynamic Bayesian networks; IR-67884

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APA (6th Edition):

Evers, S. (2009). Sensor Data Management with Probabilistic Models. (Doctoral Dissertation). University of Twente. Retrieved from https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html ; urn:nbn:nl:ui:28-67884 ; 7ae1abae-c43d-46b7-9063-95fa6375c863 ; 10.3990/1.9789036528672 ; urn:isbn:978-90-365-2867-2 ; urn:nbn:nl:ui:28-67884 ; https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html

Chicago Manual of Style (16th Edition):

Evers, S. “Sensor Data Management with Probabilistic Models.” 2009. Doctoral Dissertation, University of Twente. Accessed December 11, 2019. https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html ; urn:nbn:nl:ui:28-67884 ; 7ae1abae-c43d-46b7-9063-95fa6375c863 ; 10.3990/1.9789036528672 ; urn:isbn:978-90-365-2867-2 ; urn:nbn:nl:ui:28-67884 ; https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html.

MLA Handbook (7th Edition):

Evers, S. “Sensor Data Management with Probabilistic Models.” 2009. Web. 11 Dec 2019.

Vancouver:

Evers S. Sensor Data Management with Probabilistic Models. [Internet] [Doctoral dissertation]. University of Twente; 2009. [cited 2019 Dec 11]. Available from: https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html ; urn:nbn:nl:ui:28-67884 ; 7ae1abae-c43d-46b7-9063-95fa6375c863 ; 10.3990/1.9789036528672 ; urn:isbn:978-90-365-2867-2 ; urn:nbn:nl:ui:28-67884 ; https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html.

Council of Science Editors:

Evers S. Sensor Data Management with Probabilistic Models. [Doctoral Dissertation]. University of Twente; 2009. Available from: https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html ; urn:nbn:nl:ui:28-67884 ; 7ae1abae-c43d-46b7-9063-95fa6375c863 ; 10.3990/1.9789036528672 ; urn:isbn:978-90-365-2867-2 ; urn:nbn:nl:ui:28-67884 ; https://research.utwente.nl/en/publications/sensor-data-management-with-probabilistic-models(7ae1abae-c43d-46b7-9063-95fa6375c863).html


University of Cambridge

29. Higson, Edward John. Bayesian methods and machine learning in astrophysics.

Degree: PhD, 2019, University of Cambridge

 This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters… (more)

Subjects/Keywords: Machine Learning; Bayesian Inference; Nested sampling; Cosmology; Black Holes; Gravitational Waves; Neural Networks; Regression; Astrophysics; Sparsity; Parameter Estimation; Bayesian Evidence; Bayesian; Statistics; Bayesian Sparse Reconstruction; Computational Methods; Error Analysis; Dynamic Nested Sampling; nestcheck; perfectns; dyPolyChord; dynesty; Image Processing; Sparse Reconstruction; Planck; diagnostic tests; PolyChord; MultiNest; Hubble Space Telescope; Fitting; Nonparametric statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Higson, E. J. (2019). Bayesian methods and machine learning in astrophysics. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/289728 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929

Chicago Manual of Style (16th Edition):

Higson, Edward John. “Bayesian methods and machine learning in astrophysics.” 2019. Doctoral Dissertation, University of Cambridge. Accessed December 11, 2019. https://www.repository.cam.ac.uk/handle/1810/289728 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929.

MLA Handbook (7th Edition):

Higson, Edward John. “Bayesian methods and machine learning in astrophysics.” 2019. Web. 11 Dec 2019.

Vancouver:

Higson EJ. Bayesian methods and machine learning in astrophysics. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2019 Dec 11]. Available from: https://www.repository.cam.ac.uk/handle/1810/289728 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929.

Council of Science Editors:

Higson EJ. Bayesian methods and machine learning in astrophysics. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/289728 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929


Georgia Tech

30. Calanni Fraccone, Giorgio M. Bayesian networks for uncertainty estimation in the response of dynamic structures.

Degree: PhD, Aerospace Engineering, 2008, Georgia Tech

 The dissertation focuses on estimating the uncertainty associated with stress/strain prediction procedures from dynamic test data used in turbine blade analysis. An accurate prediction of… (more)

Subjects/Keywords: Bayesian networks; Dynamic structures; Stress prediction; Structures; Uncertainty quantification; Uncertainty estimation; Dynamic testing; Uncertainty (Information theory); Strains and stresses; Turbines Blades; Bayesian statistical decision theory

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Calanni Fraccone, G. M. (2008). Bayesian networks for uncertainty estimation in the response of dynamic structures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/24714

Chicago Manual of Style (16th Edition):

Calanni Fraccone, Giorgio M. “Bayesian networks for uncertainty estimation in the response of dynamic structures.” 2008. Doctoral Dissertation, Georgia Tech. Accessed December 11, 2019. http://hdl.handle.net/1853/24714.

MLA Handbook (7th Edition):

Calanni Fraccone, Giorgio M. “Bayesian networks for uncertainty estimation in the response of dynamic structures.” 2008. Web. 11 Dec 2019.

Vancouver:

Calanni Fraccone GM. Bayesian networks for uncertainty estimation in the response of dynamic structures. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2019 Dec 11]. Available from: http://hdl.handle.net/1853/24714.

Council of Science Editors:

Calanni Fraccone GM. Bayesian networks for uncertainty estimation in the response of dynamic structures. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/24714

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