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Artykuły w czasopismach na temat "Bayesian"
Hutchon, David J. R. "Why clinicians are natural bayesians: Bayesian confusion". BMJ 330, nr 7504 (9.06.2005): 1390.2. http://dx.doi.org/10.1136/bmj.330.7504.1390-a.
Pełny tekst źródłaDavidson, Russell. "An Agnostic Look at Bayesian Statistics and Econometrics". Review of Economic Analysis 2, nr 2 (6.08.2010): 153–68. http://dx.doi.org/10.15353/rea.v2i2.1470.
Pełny tekst źródłaEl-Gamal, Mahmoud A., i Rangarajan K. Sundaram. "Bayesian economists … Bayesian agents". Journal of Economic Dynamics and Control 17, nr 3 (maj 1993): 355–83. http://dx.doi.org/10.1016/0165-1889(93)90002-a.
Pełny tekst źródłaHicks, Tyler, Liliana Rodríguez-Campos i Jeong Hoon Choi. "Bayesian Posterior Odds Ratios". American Journal of Evaluation 39, nr 2 (23.05.2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.
Pełny tekst źródłaSchwab, Andreas, i William H. Starbuck. "Bayesian Studies: Why We All Should Be Bayesians". Academy of Management Proceedings 2018, nr 1 (sierpień 2018): 18255. http://dx.doi.org/10.5465/ambpp.2018.18255symposium.
Pełny tekst źródłaKrackhardt, David, Andreas Schwab i William H. Starbuck. "Bayesian Statistics: Why We All Should Be Bayesians". Academy of Management Proceedings 2017, nr 1 (sierpień 2017): 15147. http://dx.doi.org/10.5465/ambpp.2017.15147symposium.
Pełny tekst źródłaSHEARER, Robert, i William SHEARER. "THE BAYESIAN ANTINOMY RESOLVED". International Journal of Theology, Philosophy and Science 3, nr 5 (20.11.2019): 5–11. http://dx.doi.org/10.26520/ijtps.2019.3.5.5-11.
Pełny tekst źródłaHuang, Hening. "A new modified Bayesian method for measurement uncertainty analysis and the unification of frequentist and Bayesian inference". Journal of Probability and Statistical Science 20, nr 1 (3.10.2022): 52–79. http://dx.doi.org/10.37119/jpss2022.v20i1.515.
Pełny tekst źródłaWijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN". PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, nr 2 (1.06.2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.
Pełny tekst źródłaRIZAL, MUHAMMAD, i Sri Utami Zuliana. "FORECASTING USING SARIMA AND BAYESIAN STRUCTURAL TIME SERIES METHOD FOR RANGE SEASONAL TIME". Proceedings of The International Conference on Data Science and Official Statistics 2023, nr 1 (29.12.2023): 382–91. http://dx.doi.org/10.34123/icdsos.v2023i1.402.
Pełny tekst źródłaRozprawy doktorskie na temat "Bayesian"
Kennedy, Marc. "Bayesian quadrature and Bayesian rescaling". Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319655.
Pełny tekst źródłaNappa, Dario. "Bayesian classification using Bayesian additive and regression trees". Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3336814.
Pełny tekst źródłaTitle from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: . Adviser: Xinlei Wang. Includes bibliographical references.
Yu, Qingzhao. "Bayesian synthesis". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155324080.
Pełny tekst źródłaDuggan, John Palfrey Thomas R. Palfrey Thomas R. "Bayesian implementation /". Diss., Pasadena, Calif. : California Institute of Technology, 1995. http://resolver.caltech.edu/CaltechETD:etd-09182007-084408.
Pełny tekst źródłaFilho, Paulo Cilas Marques. "Análise bayesiana de densidades aleatórias simples". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-25052012-184549/.
Pełny tekst źródłaWe define, from a known partition in subintervals of a bounded interval of the real line, a prior distribution over a class of densities with respect to Lebesgue measure constructing a random density whose realizations are nonnegative simple functions that integrate to one and have a constant value on each subinterval of the partition. These simple random densities are used in the Bayesian analysis of a set of absolutely continuous observables and the prior distribution is proved to be closed under sampling. We explore the prior and posterior distributions through stochastic simulations and find Bayesian solutions to the problem of density estimation. Simulations results show the asymptotic behavior of the posterior distribution as we increase the size of the analyzed data samples. When the partition is unknown, we propose a choice criterion based on the information contained in the sample. In spite of the fact that the expectation of a simple random density is always a discontinuous density, we get smooth estimates solving a decision problem where the states of nature are realizations of the simple random density and the actions are smooth densities of a suitable class.
Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection". Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.
Pełny tekst źródłaTseng, Shih-Hsien. "Bayesian and Semi-Bayesian regression applied to manufacturing wooden products". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473.
Pełny tekst źródłaPramanik, Santanu. "The Bayesian and approximate Bayesian methods in small area estimation". College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8856.
Pełny tekst źródłaThesis research directed by: Joint Program in Survey Methodology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Næss, Arild Brandrud. "Bayesian Text Categorization". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9665.
Pełny tekst źródłaNatural language processing is an interdisciplinary field of research which studies the problems and possibilities of automated generation and understanding of natural human languages. Text categorization is a central subfield of natural language processing. Automatically assigning categories to digital texts has a wide range of applications in todays information societyfrom filtering spam to creating web hierarchies and digital newspaper archives. It is a discipline that lends itself more naturally to machine learning than to knowledge engineering; statistical approaches to text categorization are therefore a promising field of inquiry. We provide a survey of the state of the art in text categorization, presenting the most widespread methods in use, and placing particular emphasis on support vector machinesan optimization algorithm that has emerged as the benchmark method in text categorization in the past ten years. We then turn our attention to Bayesian logistic regression, a fairly new, and largely unstudied method in text categorization. We see how this method has certain similarities to the support vector machine method, but also differs from it in crucial respects. Notably, Bayesian logistic regression provides us with a statistical framework. It can be claimed to be more modular, in the sense that it is more open to modifications and supplementations by other statistical methods; whereas the support vector machine method remains more of a black box. We present results of thorough testing of the BBR toolkit for Bayesian logistic regression on three separate data sets. We demonstrate which of BBRs parameters are of importance; and we show that its results compare favorably to those of the SVMli ght toolkit for support vector machines. We also present two extensions to the BBR toolkit. One attempts to incorporate domain knowledge by way of the prior probability distributions of single words; the other tries to make use of uncategorized documents to boost learning accuracy.
Maezawa, Akira. "Bayesian Music Alignment". 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199430.
Pełny tekst źródłaKsiążki na temat "Bayesian"
M, Smith Adrian F., red. Bayesian theory. Chichester, Eng: Wiley, 1994.
Znajdź pełny tekst źródłaKamenica, Emir. Bayesian persuasion. Cambridge, MA: National Bureau of Economic Research, 2009.
Znajdź pełny tekst źródłaInc, ebrary, red. Bayesian econometrics. Bingley: Emerald JAI, 2008.
Znajdź pełny tekst źródłaBazett, Trefor. Bayesian Inference. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95792-6.
Pełny tekst źródłaZenker, Frank, red. Bayesian Argumentation. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5357-0.
Pełny tekst źródłavan Oijen, Marcel. Bayesian Compendium. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0.
Pełny tekst źródłaHarney, Hanns Ludwig. Bayesian Inference. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41644-1.
Pełny tekst źródłaHjort, Nils Lid, Chris Holmes, Peter Muller i Stephen G. Walker, red. Bayesian Nonparametrics. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511802478.
Pełny tekst źródłaHamada, Michael S., Alyson G. Wilson, C. Shane Reese i Harry F. Martz. Bayesian Reliability. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77950-8.
Pełny tekst źródłaHarney, Hanns L. Bayesian Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06006-3.
Pełny tekst źródłaCzęści książek na temat "Bayesian"
Basu, Sanjib. "Bayesian Robustness and Bayesian Nonparametrics". W Robust Bayesian Analysis, 223–40. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2_12.
Pełny tekst źródłaZenker, Frank. "Bayesian Argumentation: The Practical Side of Probability". W Bayesian Argumentation, 1–11. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_1.
Pełny tekst źródłaPfeifer, Niki. "On Argument Strength". W Bayesian Argumentation, 185–93. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_10.
Pełny tekst źródłaBlamey, Jonny. "Upping the Stakes and the Preface Paradox". W Bayesian Argumentation, 195–210. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_11.
Pełny tekst źródłaHahn, Ulrike, Mike Oaksford i Adam J. L. Harris. "Testimony and Argument: A Bayesian Perspective". W Bayesian Argumentation, 15–38. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_2.
Pełny tekst źródłaOaksford, Mike, i Ulrike Hahn. "Why Are We Convinced by the Ad Hominem Argument?: Bayesian Source Reliability and Pragma-Dialectical Discussion Rules". W Bayesian Argumentation, 39–58. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_3.
Pełny tekst źródłaGrabmair, Matthias, i Kevin D. Ashley. "A Survey of Uncertainties and Their Consequences in Probabilistic Legal Argumentation". W Bayesian Argumentation, 61–85. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_4.
Pełny tekst źródłaPundik, Amit. "Was It Wrong to Use Statistics in R v Clark? A Case Study of the Use of Statistical Evidence in Criminal Courts". W Bayesian Argumentation, 87–109. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_5.
Pełny tekst źródłaOlsson, Erik J. "A Bayesian Simulation Model of Group Deliberation and Polarization". W Bayesian Argumentation, 113–33. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_6.
Pełny tekst źródłaBetz, Gregor. "Degrees of Justification, Bayes’ Rule, and Rationality". W Bayesian Argumentation, 135–46. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_7.
Pełny tekst źródłaStreszczenia konferencji na temat "Bayesian"
Giner Sanz, Juan José, Montserrat García Gabaldón, Emma María Ortega Navarro, Yang Shao-Horn i Valentín Pérez Herranz. "A Labview® program for illustrating the basic concepts of Bayesian inference". W IN-RED 2019: V Congreso de Innovación Educativa y Docencia en Red. València: Editorial Universitat Politècnica de València, 2019. http://dx.doi.org/10.4995/inred2019.2019.10369.
Pełny tekst źródłaDe Ath, George, Richard M. Everson i Jonathan E. Fieldsend. "How Bayesian should Bayesian optimisation be?" W GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3463164.
Pełny tekst źródłaMahler, Ronald P. S. "Bayesian versus 'plain-vanilla Bayesian' multitarget statistics". W Defense and Security, redaktor Ivan Kadar. SPIE, 2004. http://dx.doi.org/10.1117/12.544505.
Pełny tekst źródłaHuber, Marco F. "Bayesian Perceptron: Towards fully Bayesian Neural Networks". W 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303764.
Pełny tekst źródłaBi, Xiaojun, i Shumin Zhai. "Bayesian touch". W UIST'13: The 26th Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2501988.2502058.
Pełny tekst źródłaScargle, Jeffrey D. "Bayesian blocks". W The 19th international workshop on bayesium inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381860.
Pełny tekst źródłaMansour, Yishay, Aleksandrs Slivkins, Vasilis Syrgkanis i Zhiwei Steven Wu. "Bayesian Exploration". W EC '16: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2940716.2940755.
Pełny tekst źródłaAlon, Noga, Yuval Emek, Michal Feldman i Moshe Tennenholtz. "Bayesian ignorance". W Proceeding of the 29th ACM SIGACT-SIGOPS symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1835698.1835785.
Pełny tekst źródłaCouckuyt, Ivo, Sebastian Rojas Gonzalez i Juergen Branke. "Bayesian optimization". W GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3533654.
Pełny tekst źródłaCaulfield, H. John. "Bayesian imaging". W SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, redaktorzy Suganda Jutamulia i Toshimitsu Asakura. SPIE, 1998. http://dx.doi.org/10.1117/12.326803.
Pełny tekst źródłaRaporty organizacyjne na temat "Bayesian"
Kyburg, Henry, i Jr. Bayesian and Non-Bayesian Evidential Updating. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1985. http://dx.doi.org/10.21236/ada250538.
Pełny tekst źródłaKamenica, Emir, i Matthew Gentzkow. Bayesian Persuasion. Cambridge, MA: National Bureau of Economic Research, listopad 2009. http://dx.doi.org/10.3386/w15540.
Pełny tekst źródłaBaley, Isaac, i Laura Veldkamp. Bayesian Learning. Cambridge, MA: National Bureau of Economic Research, październik 2021. http://dx.doi.org/10.3386/w29338.
Pełny tekst źródłaAndrews, Stephen A., i David E. Sigeti. Bayesian Hypothesis Testing. Office of Scientific and Technical Information (OSTI), listopad 2017. http://dx.doi.org/10.2172/1409741.
Pełny tekst źródłaSantos, Eugene, Santos Jr. i Eugene. Bayesian Knowledge-Bases. Fort Belvoir, VA: Defense Technical Information Center, sierpień 1996. http://dx.doi.org/10.21236/ada324260.
Pełny tekst źródłaBaks, Klaas, Andrew Metrick i Jessica Wachter. Bayesian Performance Evaluation. Cambridge, MA: National Bureau of Economic Research, kwiecień 1999. http://dx.doi.org/10.3386/w7069.
Pełny tekst źródłaWallstrom, Timothy C. Brittleness and Bayesian Inference. Office of Scientific and Technical Information (OSTI), sierpień 2013. http://dx.doi.org/10.2172/1090691.
Pełny tekst źródłaWallstrom, Timothy C., i David M. Higdon. Is Bayesian inference "brittle"? Office of Scientific and Technical Information (OSTI), sierpień 2013. http://dx.doi.org/10.2172/1090693.
Pełny tekst źródłaKyburg, Henry E., i Jr. The Basic Bayesian Blunder. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1989. http://dx.doi.org/10.21236/ada250978.
Pełny tekst źródłaLozano-Perez, Tomas, i Leslie Kaelbling. Effective Bayesian Transfer Learning. Fort Belvoir, VA: Defense Technical Information Center, marzec 2010. http://dx.doi.org/10.21236/ada516458.
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