Relevant bibliographies by topics / Bayesian

Contents

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

Academic literature on the topic 'Bayesian'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Bayesian"

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Hutchon, David J. R. "Why clinicians are natural bayesians: Bayesian confusion." BMJ 330, no. 7504 (June 9, 2005): 1390.2. http://dx.doi.org/10.1136/bmj.330.7504.1390-a.

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Davidson, Russell. "An Agnostic Look at Bayesian Statistics and Econometrics." Review of Economic Analysis 2, no. 2 (August 6, 2010): 153–68. http://dx.doi.org/10.15353/rea.v2i2.1470.

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Bayesians and non-Bayesians, often called frequentists, seem to be perpetually at loggerheads on fundamental questions of statistical inference. This paper takes as agnostic a stand as is possible for a practising frequentist, and tries to elicit a Bayesian answer to questions of interest to frequentists. The argument is based on my presentation at a debate organised by the Rimini Centre for Economic Analysis, between me as the frequentist “advocate”, and Christian Robert on the Bayesian side.
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El-Gamal, Mahmoud A., and Rangarajan K. Sundaram. "Bayesian economists … Bayesian agents." Journal of Economic Dynamics and Control 17, no. 3 (May 1993): 355–83. http://dx.doi.org/10.1016/0165-1889(93)90002-a.

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Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (May 23, 2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.
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Schwab, Andreas, and William H. Starbuck. "Bayesian Studies: Why We All Should Be Bayesians." Academy of Management Proceedings 2018, no. 1 (August 2018): 18255. http://dx.doi.org/10.5465/ambpp.2018.18255symposium.

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Krackhardt, David, Andreas Schwab, and William H. Starbuck. "Bayesian Statistics: Why We All Should Be Bayesians." Academy of Management Proceedings 2017, no. 1 (August 2017): 15147. http://dx.doi.org/10.5465/ambpp.2017.15147symposium.

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SHEARER, Robert, and William SHEARER. "THE BAYESIAN ANTINOMY RESOLVED." International Journal of Theology, Philosophy and Science 3, no. 5 (November 20, 2019): 5–11. http://dx.doi.org/10.26520/ijtps.2019.3.5.5-11.

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Huang, 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, no. 1 (October 3, 2022): 52–79. http://dx.doi.org/10.37119/jpss2022.v20i1.515.

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This paper proposes a new modification of the traditional Bayesian method for measurement uncertainty analysis. The new modified Bayesian method is derived from the law of aggregation of information (LAI) and the rule of transformation between the frequentist view and Bayesian view. It can also be derived from the original Bayes Theorem in continuous form. We focus on a problem that is often encountered in measurement science: a measurement gives a series of observations. We consider two cases: (1) there is no genuine prior information about the measurand, so the uncertainty evaluation is purely Type A, and (2) prior information is available and is represented by a normal distribution. The traditional Bayesian method (also known as the reformulated Bayes Theorem) fails to provide a valid estimate of standard uncertainty in either case. The new modified Bayesian method provides the same solutions to these two cases as its frequentist counterparts. The differences between the new modified Bayesian method and the traditional Bayesian method are discussed. This paper reveals that the traditional Bayesian method is not a self-consistent operation, so it may lead to incorrect inferences in some cases, such as the two cases considered. In the light of the frequentist-Bayesian transformation rule and the law of aggregation of information (LAI), the frequentist and Bayesian inference are virtually equivalent, so they can be unified, at least in measurement uncertainty analysis. The unification is of considerable interest because it may resolve the long-standing debate between frequentists and Bayesians. The unification may also lead to an indisputable, uniform revision of the GUM (Evaluation of measurement data - Guide to the expression of uncertainty in measurement (JCGM 2008)).
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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (June 1, 2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.
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RIZAL, MUHAMMAD, and 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, no. 1 (December 29, 2023): 382–91. http://dx.doi.org/10.34123/icdsos.v2023i1.402.

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Data containing seasonal patterns, the SARIMA and Bayesian Structural Time Series methods, are time series methods that can be used on this type of data. This research aims to determine the steps of the SARIMA model and Bayesian Structural Time Series, applying the SARIMA model and Structural Bayesians Time Series, get the forecasting results of the SARIMA model and Bayesian Structural Time Series with MAPE measurements. The research method used is a quantitative method applied to data on the number of PT KAI train passengers in the Java region for 2006-2019. The results of this research show that the best model for forecasting the number of PT KAI train passengers in the Java region in 2006-2019 is SARIMA (2,1,0)(0,1,2)[12] with a MAPE value of 4.77% compared to the Bayesian method structural time series [12] namely 5.25%.
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Dissertations / Theses on the topic "Bayesian"

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Kennedy, Marc. "Bayesian quadrature and Bayesian rescaling." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319655.

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Nappa, 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.

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Thesis (Ph.D. in Statistical Sciences)--S.M.U.
Title from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: . Adviser: Xinlei Wang. Includes bibliographical references.
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Yu, Qingzhao. "Bayesian synthesis." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155324080.

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Duggan, 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.

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Filho, 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/.

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Definimos, a partir de uma partição de um intervalo limitado da reta real formada por subintervalos, uma distribuição a priori sobre uma classe de densidades em relação à medida de Lebesgue construindo uma densidade aleatória cujas realizações são funções simples não negativas que assumem um valor constante em cada subintervalo da partição e possuem integral unitária. Utilizamos tais densidades aleatórias simples na análise bayesiana de um conjunto de observáveis absolutamente contínuos e provamos que a distribuição a priori é fechada sob amostragem. Exploramos as distribuições a priori e a posteriori via simulações estocásticas e obtemos soluções bayesianas para o problema de estimação de densidade. Os resultados das simulações exibem o comportamento assintótico da distribuição a posteriori quando crescemos o tamanho das amostras dos dados analisados. Quando a partição não é conhecida a priori, propomos um critério de escolha a partir da informação contida na amostra. Apesar de a esperança de uma densidade aleatória simples ser sempre uma densidade descontínua, obtemos estimativas suaves resolvendo um problema de decisão em que os estados da natureza são realizações da densidade aleatória simples e as ações são densidades suaves de uma classe adequada.
We 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.
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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.

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Tseng, 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.

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Pramanik, Santanu. "The Bayesian and approximate Bayesian methods in small area estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8856.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis 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.
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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.

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Natural 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 today’s information society—from 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 machines—an 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 BBR’s 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.

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Maezawa, Akira. "Bayesian Music Alignment." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199430.

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Books on the topic "Bayesian"

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M, Smith Adrian F., ed. Bayesian theory. Chichester, Eng: Wiley, 1994.

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Kamenica, Emir. Bayesian persuasion. Cambridge, MA: National Bureau of Economic Research, 2009.

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Inc, ebrary, ed. Bayesian econometrics. Bingley: Emerald JAI, 2008.

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Bazett, Trefor. Bayesian Inference. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95792-6.

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Zenker, Frank, ed. Bayesian Argumentation. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5357-0.

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van Oijen, Marcel. Bayesian Compendium. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55897-0.

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Harney, Hanns Ludwig. Bayesian Inference. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41644-1.

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Hjort, Nils Lid, Chris Holmes, Peter Muller, and Stephen G. Walker, eds. Bayesian Nonparametrics. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511802478.

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Hamada, Michael S., Alyson G. Wilson, C. Shane Reese, and Harry F. Martz. Bayesian Reliability. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77950-8.

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Harney, Hanns L. Bayesian Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-06006-3.

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Book chapters on the topic "Bayesian"

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Basu, Sanjib. "Bayesian Robustness and Bayesian Nonparametrics." In Robust Bayesian Analysis, 223–40. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2_12.

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Zenker, Frank. "Bayesian Argumentation: The Practical Side of Probability." In Bayesian Argumentation, 1–11. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_1.

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Pfeifer, Niki. "On Argument Strength." In Bayesian Argumentation, 185–93. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_10.

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Blamey, Jonny. "Upping the Stakes and the Preface Paradox." In Bayesian Argumentation, 195–210. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_11.

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Hahn, Ulrike, Mike Oaksford, and Adam J. L. Harris. "Testimony and Argument: A Bayesian Perspective." In Bayesian Argumentation, 15–38. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_2.

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Oaksford, Mike, and Ulrike Hahn. "Why Are We Convinced by the Ad Hominem Argument?: Bayesian Source Reliability and Pragma-Dialectical Discussion Rules." In Bayesian Argumentation, 39–58. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_3.

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Grabmair, Matthias, and Kevin D. Ashley. "A Survey of Uncertainties and Their Consequences in Probabilistic Legal Argumentation." In Bayesian Argumentation, 61–85. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_4.

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Pundik, Amit. "Was It Wrong to Use Statistics in R v Clark? A Case Study of the Use of Statistical Evidence in Criminal Courts." In Bayesian Argumentation, 87–109. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_5.

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Olsson, Erik J. "A Bayesian Simulation Model of Group Deliberation and Polarization." In Bayesian Argumentation, 113–33. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_6.

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Betz, Gregor. "Degrees of Justification, Bayes’ Rule, and Rationality." In Bayesian Argumentation, 135–46. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_7.

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Conference papers on the topic "Bayesian"

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Giner Sanz, Juan José, Montserrat García Gabaldón, Emma María Ortega Navarro, Yang Shao-Horn, and Valentín Pérez Herranz. "A Labview® program for illustrating the basic concepts of Bayesian inference." In 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.

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A pesar de la importancia de la inferencia Bayesiana y el crecimiento de la investigación Bayesiana, hoy por hoy, la mayoría de los planes de estudio de grado todavía se basan en la estadística frecuentista. Una forma de facilitar la introducción de los estudiantes al mundo Bayesiano es reforzar los conceptos básicos de la filosofía Bayesiana. En este trabajo, se presenta un programa implementado en Labview® para reforzar e ilustrar los conceptos básicos que subyacen a la inferencia Bayesiana. Este programa se puede usar en prácticas informáticas, o como un applet en línea para que los estudiantes revisen los conceptos después de clase o en un curso online masivo y abierto (MOOC, por sus siglas en inglés).
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De Ath, George, Richard M. Everson, and Jonathan E. Fieldsend. "How Bayesian should Bayesian optimisation be?" In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3463164.

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Mahler, Ronald P. S. "Bayesian versus 'plain-vanilla Bayesian' multitarget statistics." In Defense and Security, edited by Ivan Kadar. SPIE, 2004. http://dx.doi.org/10.1117/12.544505.

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Huber, Marco F. "Bayesian Perceptron: Towards fully Bayesian Neural Networks." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303764.

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Bi, Xiaojun, and Shumin Zhai. "Bayesian touch." In 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.

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Scargle, Jeffrey D. "Bayesian blocks." In 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.

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Mansour, Yishay, Aleksandrs Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu. "Bayesian Exploration." In EC '16: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2940716.2940755.

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Alon, Noga, Yuval Emek, Michal Feldman, and Moshe Tennenholtz. "Bayesian ignorance." In Proceeding of the 29th ACM SIGACT-SIGOPS symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1835698.1835785.

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Couckuyt, Ivo, Sebastian Rojas Gonzalez, and Juergen Branke. "Bayesian optimization." In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3533654.

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Caulfield, H. John. "Bayesian imaging." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Suganda Jutamulia and Toshimitsu Asakura. SPIE, 1998. http://dx.doi.org/10.1117/12.326803.

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Reports on the topic "Bayesian"

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Kyburg, Henry, and Jr. Bayesian and Non-Bayesian Evidential Updating. Fort Belvoir, VA: Defense Technical Information Center, January 1985. http://dx.doi.org/10.21236/ada250538.

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Kamenica, Emir, and Matthew Gentzkow. Bayesian Persuasion. Cambridge, MA: National Bureau of Economic Research, November 2009. http://dx.doi.org/10.3386/w15540.

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Baley, Isaac, and Laura Veldkamp. Bayesian Learning. Cambridge, MA: National Bureau of Economic Research, October 2021. http://dx.doi.org/10.3386/w29338.

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Andrews, Stephen A., and David E. Sigeti. Bayesian Hypothesis Testing. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1409741.

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Santos, Eugene, Santos Jr., and Eugene. Bayesian Knowledge-Bases. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada324260.

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Baks, Klaas, Andrew Metrick, and Jessica Wachter. Bayesian Performance Evaluation. Cambridge, MA: National Bureau of Economic Research, April 1999. http://dx.doi.org/10.3386/w7069.

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Wallstrom, Timothy C. Brittleness and Bayesian Inference. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1090691.

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Wallstrom, Timothy C., and David M. Higdon. Is Bayesian inference "brittle"? Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1090693.

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Kyburg, Henry E., and Jr. The Basic Bayesian Blunder. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada250978.

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Lozano-Perez, Tomas, and Leslie Kaelbling. Effective Bayesian Transfer Learning. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada516458.

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