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Relevant bibliographies by topics / Bayesian

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Academic literature on the topic 'Bayesian'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 (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 (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 (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

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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 (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 (2017): 15147. http://dx.doi.org/10.5465/ambpp.2017.15147symposium.

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Marcos, Vinícius Monteiro da Rocha. "OS STAKEHOLDERS DAS COOPERATIVAS." Revistaft 28, no. 131 (2024): 21. https://doi.org/10.5281/zenodo.10695021.

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O objetivo deste trabalho é, por meio da teoria proposta por Freeman (1984), identificar e analisar a relação dos stakeholders das cooperativas utilizando como referência a teoria da saliência dos stakeholders. Os dados foram coletados através de questionário online direcionado aos gestores de alto escalão da organização. Foi coletado um grupo de variáveis independentes (atributos das cooperativas) e variáveis independentes (percepção dos stakeholders) e a análise de dados será por mei

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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 (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 pure

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (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 glo

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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 (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

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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.<br>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 po

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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.<br>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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<p>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 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 categori

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

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

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

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

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Harney, Hanns Ludwig. Bayesian Inference. 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 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. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77950-8.

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

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Lesaffre, Emmanuel, and Andrew B. Lawson. Bayesian Biostatistics. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119942412.

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Bernardo, Jos M., and Adrian F. M. Smith, eds. Bayesian Theory. John Wiley & Sons, Inc., 1994. http://dx.doi.org/10.1002/9780470316870.

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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. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1306-2_12.

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Zwanzig, Silvelyn, and Rauf Ahmad. "Bayesian Modelling." In Bayesian Inference. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003221623-2.

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van Oijen, Marcel. "Bayesian Inference." In Bayesian Compendium. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-66085-6_2.

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Faulkenberry, Thomas J. "Bayesian Correlation." In Bayesian Statistics. Routledge, 2025. https://doi.org/10.4324/9781003470199-3.

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Zenker, Frank. "Bayesian Argumentation: The Practical Side of Probability." In Bayesian Argumentation. 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. 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. 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. 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. 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. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5357-0_4.

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Conference papers on the topic "Bayesian"

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Ayello, Francois, Narasi Sridhar, Gerry Koch, Vinod Khare, Abdul Wahab Al-Methen, and Shabbir Safri. "Internal Corrosion Threat Assessment of Pipelines Using Bayesian Networks." In CORROSION 2014. NACE International, 2014. https://doi.org/10.5006/c2014-3851.

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Abstract Many engineers are inclined to not trust models; this is particularly true in the field of corrosion. Suspicion comes from modeled results which are inconsistent with field data. The difference between modeled results and the real world has three reasons. First, no model is accurate in all situations. Second, the input data used to run the models is never exact. And third, the operator's knowledge of the system is often missing. In order to increase confidence and reduce the gap between modeled results and field data it is necessary to address all three sources of uncertainties. A sol

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Walker, Markus, Hayk Amirkhanian, Marco F. Huber, and Uwe D. Hanebeck. "Trustworthy Bayesian Perceptrons." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706490.

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Aifer, Maxwell, Samuel Duffield, Kaelan Donatella, et al. "Thermodynamic Bayesian Inference." In 2024 IEEE International Conference on Rebooting Computing (ICRC). IEEE, 2024. https://doi.org/10.1109/icrc64395.2024.10937023.

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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. 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 estudian

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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. ACM, 2021. http://dx.doi.org/10.1145/3449726.3463164.

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Habibeh, Mohammad, and Jeff Eldred. "Bayesian Optimization For Accelerator Tuning." In Bayesian Optimization For Accelerator Tuning. US DOE, 2024. http://dx.doi.org/10.2172/2427359.

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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. 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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Reports on the topic "Bayesian"

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

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

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Baley, Isaac, and Laura Veldkamp. Bayesian Learning. National Bureau of Economic Research, 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), 2017. http://dx.doi.org/10.2172/1409741.

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

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Baks, Klaas, Andrew Metrick, and Jessica Wachter. Bayesian Performance Evaluation. National Bureau of Economic Research, 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), 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), 2013. http://dx.doi.org/10.2172/1090693.

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

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

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