Relevant bibliographies by topics / Identification uncertainty

Contents

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

Academic literature on the topic 'Identification uncertainty'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Identification uncertainty"

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Younce, R. C., and C. E. Rohrs. "Identification with nonparametric uncertainty." IEEE Transactions on Automatic Control 37, no. 6 (1992): 715–28. http://dx.doi.org/10.1109/9.256327.

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Baliuta, S. M., P. O. Chernenko, Iu V. Kuievda, and V. P. Kuevda. "IDENTIFICATION OF MATHEMATICAL MODEL OF TURBINE GENERATOR UNIT IN PRESENCE OF UNCERTAINTY." Tekhnichna Elektrodynamika 2021, no. 1 (2021): 32–39. http://dx.doi.org/10.15407/techned2021.01.032.

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An identification procedure of mathematical model of turbine generator unit in the presence of uncertainty is studied for using in the interconnected robust control automated system. The procedure is based on “worst-case” identification approach. The controlled object is modelled by the matrix transfer function with additive uncertainty. The identification consists of two stages: first is to identify transfer function with nominal parameters with the use of prediction error minimization algorithm, second – to determine weight function in additive uncertainty model using finding the worst-case
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Hogg, Michael A., David K. Sherman, Joel Dierselhuis, Angela T. Maitner, and Graham Moffitt. "Uncertainty, entitativity, and group identification." Journal of Experimental Social Psychology 43, no. 1 (2007): 135–42. http://dx.doi.org/10.1016/j.jesp.2005.12.008.

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Giacomini, Raffaella, Toru Kitagawa, and Alessio Volpicella. "Uncertain identification." Quantitative Economics 13, no. 1 (2022): 95–123. http://dx.doi.org/10.3982/qe1671.

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Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines them with a single posterior for models that are either point‐identified or that impose nondogmatic assumptions. The output
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Choi, Eunice U., and Michael A. Hogg. "Self-uncertainty and group identification: A meta-analysis." Group Processes & Intergroup Relations 23, no. 4 (2019): 483–501. http://dx.doi.org/10.1177/1368430219846990.

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A key prediction of uncertainty-identity theory is that under conditions of high self-uncertainty, people will identify more strongly with their group. This has been supported by numerous studies. To quantify this relationship, a meta-analysis was conducted on 35 studies from 30 papers ( N = 4,657). The relationship between self-uncertainty and group identification varied significantly as a function of how psychologically real the uncertainty was, as reflected in how uncertainty was operationalized and how the study was conducted. Self-uncertainty operationalized as social identity uncertainty
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Ye, Yun, Marija Jankovic, Gül E. Kremer, and Jean-Claude Bocquet. "Managing uncertainty in potential supplier identification." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, no. 4 (2014): 339–51. http://dx.doi.org/10.1017/s0890060414000511.

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AbstractAs a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEM's products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better s
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Milanese, Mario, and Michele Taragna. "H∞ identification of “soft” uncertainty models." Systems & Control Letters 37, no. 4 (1999): 217–28. http://dx.doi.org/10.1016/s0167-6911(99)00022-5.

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Wu, S. Q., and S. S. Law. "Statistical moving load identification including uncertainty." Probabilistic Engineering Mechanics 29 (July 2012): 70–78. http://dx.doi.org/10.1016/j.probengmech.2011.09.001.

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Hogg, Michael A. "Managing Self-Uncertainty Through Group Identification." Psychological Inquiry 20, no. 4 (2009): 221–24. http://dx.doi.org/10.1080/10478400903333452.

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Corrêa, G. O., and I. Postlethwaite. "Model Uncertainty And Linear System Identification." IFAC Proceedings Volumes 18, no. 3 (1985): 115–20. http://dx.doi.org/10.1016/b978-0-08-032575-0.50022-9.

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Dissertations / Theses on the topic "Identification uncertainty"

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Sui, Liqi. "Uncertainty management in parameter identification." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2330/document.

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Afin d'obtenir des simulations plus prédictives et plus précises du comportement mécanique des structures, des modèles matériau de plus en plus complexes ont été développés. Aujourd'hui, la caractérisation des propriétés des matériaux est donc un objectif prioritaire. Elle exige des méthodes et des tests d'identification dédiés dans des conditions les plus proches possible des cas de service. Cette thèse vise à développer une méthodologie d'identification efficace pour trouver les paramètres des propriétés matériau, en tenant compte de toutes les informations disponibles. L'information utilisé
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Riefelyna, Siska. "Structural uncertainty identification using mode shape information." Thesis, University of Sussex, 2012. http://sro.sussex.ac.uk/id/eprint/43334/.

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This thesis is concerned with efficient uncertainty identification (UI) – namely the nonlinear inverse problem of establishing specific statistical properties of an uncertain structure from a practically-limited supply of low-frequency dynamic response information. An established UI approach (published in 2005) which uses Maximum Likelihood Estimation (MLE) and the Perturbation Method of uncertainty propagation is adopted for the study using (for the first time) mode shape information rather than just natural or resonant frequencies. The thesis develops a method based on the use of selected co
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Pan, Qin Aktan A. E. "System identification of constructed civil engineering structures and uncertainty /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/2531.

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Rivenbark, David. "UNCERTAINTY, IDENTIFICATION, AND PRIVACY: EXPERIMENTS IN INDIVIDUAL DECISION-MAKING." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2266.

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The alleged privacy paradox states that individuals report high values for personal privacy, while at the same time they report behavior that contradicts a high privacy value. This is a misconception. Reported privacy behaviors are explained by asymmetric subjective beliefs. Beliefs may or may not be uncertain, and non-neutral attitudes towards uncertainty are not necessary to explain behavior. This research was conducted in three related parts. Part one presents an experiment in individual decision making under uncertainty. Ellsberg s canonical two-color choice problem was used to estimate a
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Laguna, Sanz Alejandro José. "Uncertainty in Postprandial Model Identification in type 1 Diabetes." Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/37191.

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Postprandial characterization of patients with type 1 diabetes is crucial for the development of an automatic glucose control system (Artificial Pancreas). Uncertainty sources within the patient, and variability of the glucose response between patients, are a challenge for individual patients model identification leading to poor predictability with current methods. Also, continuous glucose monitors, which have been the springboard for research towards a domiciliary artificial pancreas, still introduce large measurement errors, greatly complicating the characterization of the patient. In this
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Bogodorova, Tetiana. "Modeling, Model Validation and Uncertainty Identification for Power System Analysis." Doctoral thesis, KTH, Elkraftteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218008.

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It is widely accepted that correct system modeling and identification are among the most important issues power system operators face when managing instability and post-contingency scenarios. The latter is usually performed involving special computational tools that allow the operator to forecast, prevent system failure and take appropriate actions according to protocols for different contingency cases in the system. To ensure that operators make the correct simulation-based decisions, the power system models have to be validated continuously. This thesis investigates power system modeling, id
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Gökçe, Hasan Burak. "Structural identification through monitoring, modeling and predictive analysis under uncertainty." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5222.

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Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id fr
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Douglas, Joel S. (Joel Seth). "Identification of parametric uncertainty for the control of flexible structures." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36562.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 152-158).<br>by Joel S. Douglas.<br>Ph.D.
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Hohman, Zachary P. "Fearing the Uncertain: A Causal Exploration of Self-Esteem, Self-Uncertainty, and Mortality Salience." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cgu_etd/26.

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Social identity theory (Tajfel & Turner, 1979) is one of the most influential social psychological theories of group behavior and intergroup relations. Early social identity research focused on many different group processes; however, the motivation behind group identification was not fully explored. Researchers have proposed a variety of accounts for why people join and identify with groups. This dissertation unravels the relationship between, on the one hand, mortality salience, self-related uncertainty and self-esteem, and on the other group identification and ingroup defense. The general h
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Zakharov, I. P., O. A. Botsiura, and V. Semenikhin. "Instrumental uncertainties accounting in identification of nonpolynomial calibration dependence." Thesis, "Софттрейд", 2020. https://openarchive.nure.ua/handle/document/18984.

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Books on the topic "Identification uncertainty"

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Mukhopadhyay, Suparno. Structural Identification, Health Monitoring and Uncertainty Quantification under Incomplete Information with Minimal Requirements for Identifiability. [publisher not identified], 2015.

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Brenner, Marty. Aeroservoelastic uncertainity model identification from flight data. National Aeronautics and Space Administration, Dryden Flight Research Center, 2001.

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NASA Dryden Flight Research Center., ed. Aeroservoelastic uncertainity model identification from flight data. National Aeronautics and Space Administration, Dryden Flight Research Center, 2001.

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Office, General Accounting. Homeland security: Key elements to unify efforts are underway but uncertainty remains : report to congressional requesters. GAO, 2002.

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Vries, Douwe Klaus de. Identification of model uncertainty for control design. 1994.

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Manski, Charles F. Patient Care under Uncertainty. Princeton University Press, 2019. http://dx.doi.org/10.23943/princeton/9780691194738.001.0001.

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Although uncertainty is a common element of patient care, it has largely been overlooked in research on evidence-based medicine. This book strives to correct this glaring omission. Applying the tools of economics to medical decision making, the book shows how uncertainty influences every stage, from risk analysis to treatment, and how this can be reasonably confronted. In the language of econometrics, uncertainty refers to the inadequacy of available evidence and knowledge to yield accurate information on outcomes. In the context of health care, a common example is a choice between periodic su
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Moments of uncertainty in therapeutic practice: Interpreting within the matrix of projective identification, countertransference, and enactment. Columbia University Press, 2011.

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Baldi, Cindi, Caroline Bartel, and Janet Dukerich. Fostering Stakeholder Identification Through Expressed Organizational Identities. Edited by Michael G. Pratt, Majken Schultz, Blake E. Ashforth, and Davide Ravasi. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199689576.013.1.

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A vital task for organizations is to communicate their organizational identity in ways that generate organizational images that are attractive to external stakeholders. Such favorable evaluations may generate stakeholder identification—a perception of belonging such that an organization becomes important to stakeholders’ sense of self. This chapter reviews and integrates prior research to elaborate how organizational communications via mass communication channels (e.g., company websites) can generate stakeholder identification. Several motivational pathways are outlined involving stakeholders’
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Gordalla, Barbara. Social Belonging - A Cue to Success?! Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.405.

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Currently, the percentage of female students in STEM majors is about 17% lower than in other majors. What factors lead to this situation? A variety of factors impact student choice and persistence. The underlying master's thesis examined the relationships between gender, social belonging, belonging uncertainty, stereotype fit, and chilly climate. In particular, the focus was on exploring the influence of social belonging on selected success parameters (identification, expectation of success, turnover intention, grade point average, and number of third attempts to pass an exam performance). It
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Mathiesen, Amber, and Kali Roy. Common Perinatal Genetic Counseling Situations. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190681098.003.0009.

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This chapter highlights common situations in perinatal counseling, including challenging circumstances, complex situations, and ethical dilemmas. It discusses conflicts that may arise from pregnancy termination; the complexities surrounding the identification of incidental findings such as misattributed paternity, discovery of consanguinity, or discovery of an incidental condition; and issues surrounding privacy and confidentiality, including familial implications of genetic knowledge. Also discussed are difficult circumstances such as couples in conflict or nonparticipation of a male partner
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Book chapters on the topic "Identification uncertainty"

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Åström, Karl J. "Model Uncertainty and Feedback." In Iterative Identification and Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0205-2_4.

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Van den Hof, Paul. "Model Uncertainty Bounding." In Modelling and Identification with Rational Orthogonal Basis Functions. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-178-4_7.

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Kalyagin, V. A., A. P. Koldanov, P. A. Koldanov, and P. M. Pardalos. "Uncertainty of Network Structure Identification." In SpringerBriefs in Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60293-2_4.

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Wang, P., F. Pierron, O. T. Thomsen, M. Rossi, and P. Lava. "Uncertainty Quantification in VFM Identification." In Experimental and Applied Mechanics, Volume 6. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06989-0_18.

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Hayat, Abdullah Aamir, Shraddha Chaudhary, Riby Abraham Boby, et al. "Uncertainty and Sensitivity Analysis." In Vision Based Identification and Force Control of Industrial Robots. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6990-3_3.

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Patra, Sudip, and Partha Ghose. "Modelling Decision Making Uncertainty and Contextuality." In Partial Identification in Econometrics and Related Topics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59110-5_18.

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Büche, Dirk, Sönke Klostermann, and Martin Schmelzer. "Uncertainties Identification and Quantification." In Uncertainty Management for Robust Industrial Design in Aeronautics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77767-2_41.

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Vicino, A., and G. Zappa. "Adaptive Approximation of Uncertainty Sets for Linear Regression Models." In Bounding Approaches to System Identification. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9545-5_10.

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Milanese, M., and A. Vicino. "Estimation Theory for Nonlinear Models and Set Membership Uncertainty." In Bounding Approaches to System Identification. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9545-5_22.

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Djaferis, T. E., and C. V. Hollot. "Shaping Conditions and the Stability of Systems with Parameter Uncertainty." In Robustness in Identification and Control. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-9552-6_15.

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Conference papers on the topic "Identification uncertainty"

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Velo, Alessandro, Federico Fonte, Marco Favale, et al. "Uncertainty Estimation in Experimental Whirl Flutter Modes Identification." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-239.

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This paper deals with the uncertainty estimation of identified frequency and damping trends of whirl flutter modes, obtained by applying system identification methods on experimental data. In particular, two different identification approaches are considered, namely the free-decay analysis by using Matrix Pencil algorithm and the Data-Driven Stochastic Subspace Identification method (SSI), applied to system response to stochastic input. The two approaches lead to as many uncertainty estimation methodologies, both leveraging the bootstrapping statistical process. A full validation procedure is
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Zhang, Qingxin, Haoyan Wei, and Yang Qian. "Group-CLIP Uncertainty Modeling for Group Re-Identification." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887627.

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Song, Chunli, Yucan Zhou, Wenqiu Zhu, Jiang Zhou, Xiaoyan Gu, and Bo Li. "Noise-Aware Person Re-identification via Local Uncertainty Estimation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650459.

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Ferah, Mehmet Ali, and Tufan Kumbasar. "Introducing Interval Neural Networks for Uncertainty-Aware System Identification." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017048.

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Rong, Xuanman, Yuxiong Huang, Gengfeng Li, Zihao Wu, and Chenxi Wang. "Risk-Informed Vulnerability Identification for Transmission Systems under High Uncertainty." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991824.

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Younce, R. C., and C. E. Rohrs. "Identification with nonparametric uncertainty." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203372.

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Vu, Minh Thanh, Tobias J. Oechtering, Mikael Skoglund, and Holger Boche. "Uncertainty in Identification Systems." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437760.

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Shukayev, Dulat N., Zhanar Bimurat, Darkhan Abdibekov, and Nazgul O. Yergaliyeva. "Making Investment Decisions Under Uncertainty." In Modelling, Simulation and Identification / 854: Intelligent Systems and Control. ACTAPRESS, 2017. http://dx.doi.org/10.2316/p.2017.853-009.

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Wang, Richard, Ravi Shroff, Yilong Zha, Srinivasan Seshan, and Manuela Veloso. "Indoor trajectory identification: Snapping with uncertainty." In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015. http://dx.doi.org/10.1109/iros.2015.7354066.

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Scheid, R., D. Bayard, R. Chiang, and E. Mettler. "Structured uncertainty identification and control synthesis." In Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-3353.

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Reports on the topic "Identification uncertainty"

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Karpius, Peter. Nuclide Identification, Quantification, and Uncertainty. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1782632.

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Ballas, J. A., and M. J. Sliwinski. Causal Uncertainty in the Identification of Environmental Sounds. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada175228.

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Silgado-Gómez, Edgar. Sovereign uncertainty. Banco de España, 2024. http://dx.doi.org/10.53479/36875.

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This paper investigates the impact and the transmission of uncertainty regarding the future path of government finances on economic activity. I first employ a data-rich approach to extract a novel proxy that captures uncertainty surrounding public finances, which I refer to as sovereign uncertainty, and demonstrate that the estimated measure exhibits distinct fluctuations from macro-financial and economic policy uncertainty indices. Next, I analyse the behaviour of sovereign uncertainty shocks and detect the presence of significant and long-lasting negative effects in the financial and macroec
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Ballas, James A. Implicit Knowledge in the Identification of Environmental Sounds: Causal Uncertainty and Stereotype. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada214940.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn
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Hunke, Elizabeth Clare, Jorge Rolando Urrego Blanco, and Nathan Mark Urban. Uncertainty, Sensitivity Analysis, and Causal Identification in the Arctic using a Perturbed Parameter Ensemble of the HiLAT Climate Model. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1422910.

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Bergman, Lawrence A., Alexander F. Vakakis, and D. M. McFarland. Towards Development of Nonparametric System Identification Base Based on Slow-Flow Dynamics, with Application to Damage Detection and Uncertainty Quantification. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada548212.

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Volpicella, Alessio, Toru Kitagawa, and Raffaella Giacomini. Uncertain identification. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.1817.

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Volpicella, Alessio, Toru Kitagawa, and Raffaella Giacomini. Uncertain Identification. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.3320.

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McCarthy, Nancy, Paul Winters, Ana María Linares, and Timothy Essam. Indicators to Assess the Effectiveness of Climate Change Projects. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0009014.

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Determining reasonable indicators for climate change projects is complicated by the long-term horizon of both mitigation and adaptation project impacts as well as the uncertainty associated with climate change impacts. Actions taken now are often designed to have an impact in the uncertain and distant future and may not directly mitigate or adapt to climate change, but be taken as a step to prepare for future actions. Further complicating identification of indicators is the fact that there is a spectrum of projects, from the pure climate change-focused projects to those that provide climate ch
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