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Статті в журналах з теми "Bayesian paradigms"
Liu, Zhi-Qiang. "Bayesian Paradigms in Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (February 1997): 3–33. http://dx.doi.org/10.1142/s0218001497000020.
Повний текст джерелаOaksford, Mike, and Nick Chater. "New Paradigms in the Psychology of Reasoning." Annual Review of Psychology 71, no. 1 (January 4, 2020): 305–30. http://dx.doi.org/10.1146/annurev-psych-010419-051132.
Повний текст джерелаNeupert, Shevaun D., Claire M. Growney, Xianghe Zhu, Julia K. Sorensen, Emily L. Smith, and Jan Hannig. "BFF: Bayesian, Fiducial, and Frequentist Analysis of Cognitive Engagement among Cognitively Impaired Older Adults." Entropy 23, no. 4 (April 6, 2021): 428. http://dx.doi.org/10.3390/e23040428.
Повний текст джерелаLy, Alexander, Akash Raj, Alexander Etz, Maarten Marsman, Quentin F. Gronau, and Eric-Jan Wagenmakers. "Bayesian Reanalyses From Summary Statistics: A Guide for Academic Consumers." Advances in Methods and Practices in Psychological Science 1, no. 3 (August 13, 2018): 367–74. http://dx.doi.org/10.1177/2515245918779348.
Повний текст джерелаBojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing missing always at random in multivariate data." Biometrika 107, no. 1 (November 23, 2019): 246–53. http://dx.doi.org/10.1093/biomet/asz061.
Повний текст джерелаAlotaibi, Refah, Lamya A. Baharith, Ehab M. Almetwally, Mervat Khalifa, Indranil Ghosh, and Hoda Rezk. "Statistical Inference on a Finite Mixture of Exponentiated Kumaraswamy-G Distributions with Progressive Type II Censoring Using Bladder Cancer Data." Mathematics 10, no. 15 (August 7, 2022): 2800. http://dx.doi.org/10.3390/math10152800.
Повний текст джерелаNeupert, Shevaun D., and Jan Hannig. "BFF: Bayesian, Fiducial, Frequentist Analysis of Age Effects in Daily Diary Data." Journals of Gerontology: Series B 75, no. 1 (August 17, 2019): 67–79. http://dx.doi.org/10.1093/geronb/gbz100.
Повний текст джерелаKabanda, Gabriel. "Bayesian Network Model for a Zimbabwean Cybersecurity System." Oriental journal of computer science and technology 12, no. 4 (January 3, 2020): 147–67. http://dx.doi.org/10.13005/ojcst12.04.02.
Повний текст джерелаLaurens, Jean, Dominik Straumann, and Bernhard J. M. Hess. "Processing of Angular Motion and Gravity Information Through an Internal Model." Journal of Neurophysiology 104, no. 3 (September 2010): 1370–81. http://dx.doi.org/10.1152/jn.00143.2010.
Повний текст джерелаGuo, Jeff, Bojana Ranković, and Philippe Schwaller. "Bayesian Optimization for Chemical Reactions." CHIMIA 77, no. 1/2 (February 22, 2023): 31. http://dx.doi.org/10.2533/chimia.2023.31.
Повний текст джерелаДисертації з теми "Bayesian paradigms"
Taheri, Sona. "Learning Bayesian networks based on optimization approaches." Thesis, University of Ballarat, 2012. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/36051.
Повний текст джерелаDoctor of Philosophy
Cordani, Lisbeth Kaiserlian. "O ensino de estatística na universidade e a controvérsia sobre os fundamentos da inferência." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/48/48134/tde-04072011-084602/.
Повний текст джерелаIn general most of the undergraduate courses in Brazil offer a basic discipline on probability and statistics. Beyond the descriptive procedures, associated with data analysis, these courses present to the students some inferential techniques, usually linked to the classical (frequentist) Neyman-Pearson school. It is not common to present the inferential aspects from the Bayesian point of view. Everybody knows that both student and teacher have problems with this basic discipline. The student, because he/she receives, in general, a mechanical course, without motivation, with no links to their other disciplines, and the teacher, because he/she usulally teaches to very naïve students concerning concept like uncertainty and variability. Added to that, students seem to have some fear towards the discipline (taboo). In order to discuss the first inferential notions presented in this discipline, and to answer the question which inference should we teach in a basic discipline of statistics to undergraduate students? we have tried, in this work, to characterise the relationship between statistics and the following aspects: scientific creation in general and empirism and rationalism in particular; the existence or not of a scientific method; objectivism and subjectivism; the paradigms associated to the classical and to the Bayesian schools; learning and some cognitive aspects. We have compared the inferential approaches, and some examples have been presented. This work suggests that the first program of a basic discipline of probability and statistics should include some epistemological inferential aspects as well as the introduction of inferential statistics by means of both approaches: classical and Bayesian. This action will prevent, at least at the first contact, the members of the Bayesian school from proposing the rupture with the classical, and also the members of the classical one from maintaining the status quo. In fact, the proposal is of coexistence of both schools in a first level, because we think it is a teachers duty to show the state of art to his/her students, giving the possibility of option (if necessary) for a following step.
Lee, T. D. "Implementation of the Bayesian paradigm for highly parameterised linear models." Thesis, University of Nottingham, 1986. http://eprints.nottingham.ac.uk/14421/.
Повний текст джерелаSzymczak, Marcin. "Programming language semantics as a foundation for Bayesian inference." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28993.
Повний текст джерелаLikhari, Amitoj S. "Computational accounts of attentional bias neural network and Bayesian network models of the dot probe paradigm /." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0009481.
Повний текст джерелаKliethermes, Stephanie Ann. "A Bayesian nonparametric approach to modeling longitudinal growth curves with non-normal outcomes." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/2546.
Повний текст джерелаAjili, Moez. "Reliability of voice comparison for forensic applications." Thesis, Avignon, 2017. http://www.theses.fr/2017AVIG0223/document.
Повний текст джерелаIt is common to see voice recordings being presented as a forensic trace in court. Generally, a forensic expert is asked to analyse both suspect and criminal’s voice samples in order to indicate whether the evidence supports the prosecution (same-speaker) or defence (different-speakers) hypotheses. This process is known as Forensic Voice Comparison (FVC). Since the emergence of the DNA typing model, the likelihood-ratio (LR) framework has become the new “golden standard” in forensic sciences. The LR not only supports one of the hypotheses but also quantifies the strength of its support. However, the LR accepts some practical limitations due to its estimation process itself. It is particularly true when Automatic Speaker Recognition (ASpR) systems are considered as they are outputting a score in all situations regardless of the case specific conditions. Indeed, several factors are not taken into account by the estimation process like the quality and quantity of information in both voice recordings, their phonological content or also the speakers intrinsic characteristics, etc. All these factors put into question the validity and reliability of FVC. In this Thesis, we wish to address these issues. First, we propose to analyse how the phonetic content of a pair of voice recordings affects the FVC accuracy. We show that oral vowels, nasal vowels and nasal consonants bring more speaker-specific information than averaged phonemic content. In contrast, plosive, liquid and fricative do not have a significant impact on the LR accuracy. This investigation demonstrates the importance of the phonemic content and highlights interesting differences between inter-speakers effects and intra-speaker’s ones. A further study is performed in order to study the individual speaker-specific information for each vowel based on formant parameters without any use of ASpR system. This study has revealed interesting differences between vowels in terms of quantity of speaker information. The results show clearly the importance of intra-speaker variability effects in FVC reliability estimation. Second, we investigate an approach to predict the LR reliability based only on the pair of voice recordings. We define a homogeneity criterion (NHM) able to measure the presence of relevant information and the homogeneity of this information between the pair of voice recordings. We are expecting that lowest values of homogeneity are correlated with the lowest LR’s accuracy measures, as well as the opposite behaviour for high values. The results showed the interest of the homogeneity measure for FVC reliability. Our studies reported also large differences of behaviour between FVC genuine and impostor trials. The results confirmed the importance of intra-speaker variability effects in FVC reliability estimation. The main takeaway of this Thesis is that averaging the system behaviour over a high number of factors (speaker, duration, content...) hides potentially many important details. For a better understanding of FVC approach and/or an ASpR system, it is mandatory to explore the behaviour of the system at an as-detailed-as-possible scale (The devil lies in the details)
Rodrigues, Agatha Sacramento. "Estatística em confiabilidade de sistemas: uma abordagem Bayesiana paramétrica." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10102018-232055/.
Повний текст джерелаThe reliability of a system of components depends on reliability of each component. Thus, the initial statistical work should be the estimation of the reliability of each component of the system. This is not an easy task because when the system fails, the failure time of a given component can be not observed, that is, a problem of censored data. We propose parametric Bayesian models for reliability functions estimation of systems and components involved in four scenarios. First, a Weibull model is proposed to estimate component failure time distribution from non-repairable coherent systems when there are available the system failure time and the component status at the system failure moment. Furthermore, identically distributed failure times are not a required restriction. An important result is proved: without the assumption that components\' lifetimes are mutually independent, a given set of sub-reliability functions does not identify the corresponding marginal reliability function. In masked cause of failure situations, it is not possible to identify the statuses of the components at the moment of system failure and, in this second scenario, we propose a Bayesian Weibull model by means of latent variables in the estimation process. The two models described above propose to estimate marginally the reliability functions of the components when the information of the other components is not available or necessary and, consequently, the assumption of independence among the components\' failure times is necessary. In order to not impose this assumption, the Hougaard multivariate Weibull model is proposed for the estimation of the components\' reliability functions involved in non-repairable coherent systems. Finally, a Weibull model for the estimation of the reliability functions of components of a repairable series system with masked cause of failure is proposed. For each scenario, different simulation studies are carried out to evaluate the proposed models, always comparing then with the best solution found in the literature until then. In general, the proposed models present better results. In order to demonstrate the applicability of the models, data analysis are performed with real problems not only from the reliability area, but also from social area.
SARCIA', SALVATORE ALESSANDRO. "An Approach to improving parametric estimation models in the case of violation of assumptions based upon risk analysis." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/1048.
Повний текст джерелаWoo, Gloria. "Evolving Paradigms in the Treatment of Hepatitis B." Thesis, 2010. http://hdl.handle.net/1807/32961.
Повний текст джерелаКниги з теми "Bayesian paradigms"
Titelbaum, Michael G. Fundamentals of Bayesian Epistemology 2. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192863140.001.0001.
Повний текст джерелаTitelbaum, Michael G. Fundamentals of Bayesian Epistemology 1. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198707608.001.0001.
Повний текст джерелаData Mining : Foundations and Intelligent Paradigms : VOLUME 2: Statistical, Bayesian, Time Series and Other Theoretical Aspects. Springer Berlin / Heidelberg, 2014.
Знайти повний текст джерелаJain, Lakhmi C., and Dawn E. Holmes. Data Mining : Foundations and Intelligent Paradigms Vol. 2 : VOLUME 2: Statistical, Bayesian, Time Series and Other Theoretical Aspects. Springer, 2011.
Знайти повний текст джерелаNewen, Albert, Leon De Bruin, and Shaun Gallagher, eds. The Oxford Handbook of 4E Cognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198735410.001.0001.
Повний текст джерелаGamerman, Dani, Tufi M. Soares, and Flávio Gonçalves. Bayesian analysis in item response theory applied to a large-scale educational assessment. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.22.
Повний текст джерелаWestheimer, Gerald. The Shifted-Chessboard Pattern as Paradigm of the Exegesis of Geometrical-Optical Illusions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0036.
Повний текст джерелаЧастини книг з теми "Bayesian paradigms"
Bandyopadhyay, Prasanta S., Gordon Brittan, and Mark L. Taper. "Bayesian and Evidential Paradigms." In SpringerBriefs in Philosophy, 15–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27772-1_2.
Повний текст джерелаBiggio, Battista, Giorgio Fumera, and Fabio Roli. "Bayesian Linear Combination of Neural Networks." In Innovations in Neural Information Paradigms and Applications, 201–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04003-0_9.
Повний текст джерелаMcDougall, Damon, and Chris K. R. T. Jones. "Decreasing Flow Uncertainty in Bayesian Inverse Problems Through Lagrangian Drifter Control." In Mathematical Paradigms of Climate Science, 215–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39092-5_10.
Повний текст джерелаHruschka, Estevam R., and Maria do Carmo Nicoletti. "Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation." In Emerging Paradigms in Machine Learning, 75–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28699-5_5.
Повний текст джерелаWinkler, Gerhard. "The Bayesian Paradigm." In Image Analysis, Random Fields and Dynamic Monte Carlo Methods, 13–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-97522-6_2.
Повний текст джерелаLongford, Nicholas T. "The Bayesian Paradigm." In Statistical Decision Theory, 49–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40433-7_4.
Повний текст джерелаWinkler, Gerhard. "The Bayesian Paradigm." In Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, 9–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6_2.
Повний текст джерелаTellaeche, Alberto, Xavier-P. BurgosArtizzu, Gonzalo Pajares, and Angela Ribeiro. "A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms." In Advances in Soft Computing, 72–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74972-1_11.
Повний текст джерелаVidakovic, Brani. "Γ-Minimax: A Paradigm for Conservative Robust Bayesians." In Robust Bayesian Analysis, 241–59. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2_13.
Повний текст джерелаVallverdú, Jordi. "The Coevolution, Battles, and Fights of Both Paradigms." In Bayesians Versus Frequentists, 61–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48638-2_5.
Повний текст джерелаТези доповідей конференцій з теми "Bayesian paradigms"
Ocampo, Shirlee, Rechel Arcilla, Frumencio Co, Ryan Jumangit, and Felipe Diokno. "Enthusing students towards statistical literacy using transformative learning paradigm: implementation and appraisal." In Statistics education for Progress: Youth and Official Statistics. IASE international Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.113201.
Повний текст джерелаGuijarro, Maria, Gonzalo Pajares, Raquel Abreu, Luis Garmendia, and Matilde Santos. "Design of a Hybrid Classifier for Natural Textures in Images from the Bayesian and Fuzzy Paradigms." In 2007 IEEE International Symposium on Intelligent Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/wisp.2007.4447562.
Повний текст джерелаMusharraf, Mashrura, Allison Moyle, Faisal Khan, and Brian Veitch. "Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78420.
Повний текст джерелаLu, Zhichao, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, and Vishnu Naresh Boddeti. "NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/659.
Повний текст джерелаSnover, Matthew G., and Michael R. Brent. "A Bayesian model for morpheme and paradigm identification." In the 39th Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073012.1073075.
Повний текст джерелаWang, Pingfeng, Byeng D. Youn, Zhimin Xi, and Artemis Kloess. "Bayesian Reliability Analysis With Evolving, Insufficient, and Subjective Data Sets." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49663.
Повний текст джерелаStangl, Dalene. "Design of an internet course for training medical researchers in Bayesian statistical methods." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00204.
Повний текст джерелаArasaratnam, Ienkaran, Jimi Tjong, and Ryan Ahmed. "Battery management system in the Bayesian paradigm: Part I: SOC estimation." In 2014 IEEE Transportation Electrification Conference and Expo (ITEC). IEEE, 2014. http://dx.doi.org/10.1109/itec.2014.6861863.
Повний текст джерелаBhoyar, A., S. Sharma, S. Barve, and R. Kumar Rana. "Intelligent Control of Autonomous Vessels: Bayesian Estimation Instead of Statistical Learning?" In International Conference on Marine Engineering and Technology Oman. London: IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.008.
Повний текст джерелаChoudhury, Sanjiban, Siddhartha Srinivasa, and Sebastian Scherer. "Bayesian Active Edge Evaluation on Expensive Graphs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/679.
Повний текст джерела