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Journal articles on the topic 'Bayesian'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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

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12

Withers, Suzanne Davies. "Quantitative methods: Bayesian inference, Bayesian thinking." Progress in Human Geography 26, no. 4 (2002): 553–66. http://dx.doi.org/10.1191/0309132502ph386pr.

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13

Fienberg, Stephen E. "When did Bayesian inference become "Bayesian"?" Bayesian Analysis 1, no. 1 (2006): 1–40. http://dx.doi.org/10.1214/06-ba101.

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14

Kyburg, Henry E. "Bayesian and non-bayesian evidential updating." Artificial Intelligence 31, no. 3 (1987): 271–93. http://dx.doi.org/10.1016/0004-3702(87)90068-3.

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15

Shim, Heejung, and Bret Larget. "BayesCAT: Bayesian co-estimation of alignment and tree." Biometrics 74, no. 1 (2017): 270–79. http://dx.doi.org/10.1111/biom.12640.

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16

Ferreira, Thales Rangel, Luiz Alberto Beijo, Gilberto Rodrigues Liska, and Giulia Eduarda Bento. "MODELAGEM BAYESIANA DA TEMPERATURA MÁXIMA DO AR EM DIVINÓPOLIS-MG." Nativa 12, no. 3 (2024): 449–56. http://dx.doi.org/10.31413/nat.v12i3.17665.

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Nesta pesquisa, objetivou-se modelar o comportamento da temperatura máxima trimestral da cidade de Divinópolis-MG, ajustando a distribuição Generalizada de Valores Extremos às séries históricas de temperaturas máximas através de dois métodos distintos: Máxima Verossimilhança (MV) e Inferência Bayesiana. Objetivou-se também, para cada tempo de retorno, calcular os níveis de retorno de temperatura máxima da referida localidade, avaliando a acurácia e o erro médio de predição (EMP). Para o cálculo dos níveis de retorno foram utilizados o método de MV e abordagens Bayesianas utilizando diferentes

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17

Na, Jonghyun, Taekseon Ryu, Joonmyoung Kim, Hansuk Kim, Manjae Kwon, and Yongsung Joo. "A Bayesian Spatial Contamination Model." Korean Data Analysis Society 24, no. 3 (2022): 919–31. http://dx.doi.org/10.37727/jkdas.2022.24.3.919.

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In environmental research, it is often the case that to cluster observations into environmentally polluted and natural groups is an important issue. The Bayesian contamination model which adopts a multivariate mixture regression model has been developed in that it aims to cluster observations and estimate the average amount of pollution. However, because the Bayesian contamination model does not take spatial correlations between observations into consideration, a Bayesian spatial contamination model is proposed. A simulation study was conducted showing that the proposed model has an advantage

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18

Mansour, Yishay, Alex Slivkins, Vasilis Syrgkanis, and Zhiwei Steven Wu. "Bayesian Exploration: Incentivizing Exploration in Bayesian Games." Operations Research 70, no. 2 (2022): 1105–27. http://dx.doi.org/10.1287/opre.2021.2205.

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In a wide range of recommendation systems, self-interested individuals (“agents”) make decisions over time, using information revealed by other agents in the past, and producing information that may help agents in the future. Each agent would like to exploit the best action given the current information but would prefer the previous agents to explore various alternatives to collect information. A social planner, by means of a well-designed recommendation policy, can incentivize the agents to balance exploration and exploitation in order to maximize social welfare or some other objective. The r

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19

Gao, Xiaoguang, Yu Yang, and Zhigao Gao. "Learning Bayesian networks by constrained Bayesian estimation." Journal of Systems Engineering and Electronics 30, no. 03 (2019): 511–24. http://dx.doi.org/10.21629/jsee.2019.03.09.

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20

Gopnik, Alison, and Joshua B. Tenenbaum. "Bayesian networks, Bayesian learning and cognitive development." Developmental Science 10, no. 3 (2007): 281–87. http://dx.doi.org/10.1111/j.1467-7687.2007.00584.x.

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21

Lecouteux, Guilhem. "Bayesian game theorists and non-Bayesian players." European Journal of the History of Economic Thought 25, no. 6 (2018): 1420–54. http://dx.doi.org/10.1080/09672567.2018.1523207.

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22

Forbes, Florence, and Adrian E. Raftery. "Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis." Journal of the American Statistical Association 94, no. 446 (1999): 555–68. http://dx.doi.org/10.1080/01621459.1999.10474150.

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23

Reyad, Hesham, Adil Mousa Younis, and Amal Alsir Alkhedir. "Comparison of estimates using censored samples from Gompertz model: Bayesian, E-Bayesian, hierarchical Bayesian and empirical Bayesian schemes." International Journal of Advanced Statistics and Probability 4, no. 1 (2016): 47. http://dx.doi.org/10.14419/ijasp.v4i1.5914.

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<p>This paper aims to introduce a comparative study for the E-Bayesian criteria with three various Bayesian approaches; Bayesian, hierarchical Bayesian and empirical Bayesian. This study is concerned to estimate the shape parameter and the hazard function of the Gompertz distribution based on type-II censoring. All estimators are obtained under symmetric loss function [squared error loss (SELF))] and three different asymmetric loss functions [quadratic loss function (QLF), entropy loss function (ELF) and LINEX loss function (LLF)]. Comparisons among all estimators are achieved in terms o

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24

Poirier, Dale J. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics." Journal of Economic Perspectives 2, no. 1 (1988): 121–44. http://dx.doi.org/10.1257/jep.2.1.121.

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I plan to discuss, in as simple and nontechnical a fashion as possible, the subjectivist-Bayesian attitude toward model building in econometrics and to contrast it with the standard frequentist attitude. To convey what I believe is the principle distinguishing attitude between Bayesians and non-Bayesians, I refer to their respective positions as “subjectivist” and “frequentist.” The basic differences between these positions arise from different interpretations of “probability.” Frequentists interpret probability as a property of the external world, i.e., the limiting relative frequency of the

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25

DAMODARAN, D., B. RAVIKUMAR, and VELIMUTHU RAMACHANDRAN. "BAYESIAN SOFTWARE RELIABILITY MODEL COMBINING TWO PRIORS AND PREDICTING TOTAL NUMBER OF FAILURES AND FAILURE TIME." International Journal of Reliability, Quality and Safety Engineering 21, no. 06 (2014): 1450031. http://dx.doi.org/10.1142/s0218539314500314.

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Reliability statistics is divided into two mutually exclusive camps and they are Bayesian and Classical. The classical statistician believes that all distribution parameters are fixed values whereas Bayesians believe that parameters are random variables and have a distribution of their own. Bayesian approach has been applied for the Software Failure data and as a result of that several Bayesian Software Reliability Models have been formulated for the last three decades. A Bayesian approach to software reliability measurement was taken by Littlewood and Verrall [A Bayesian reliability growth mo

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26

DEL ÁGUILA, ISABEL MARÍA, and JOSÉ DEL SAGRADO. "REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS." International Journal of Software Engineering and Knowledge Engineering 21, no. 02 (2011): 167–90. http://dx.doi.org/10.1142/s0218194011005219.

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Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification perform

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27

Peters, Sunday O., Kadir Kızılkaya, Mahmut Sinecen, Burcu Mestav, Aranganoor K. Thiruvenkadan, and Milton G. Thomas. "Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population." Animals 13, no. 7 (2023): 1272. http://dx.doi.org/10.3390/ani13071272.

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The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the

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28

Hamimes, Ahmed, Rachid Benamirouche, and Fatih Chellai. "Bayesian methods in applied econometrics, or advantages and properties of Bayesian." Journal of Economic Growth and Entrepreneurship 5, no. 1 (2021): 1–10. https://doi.org/10.5281/zenodo.4459651.

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<em>Bayesian statistics can be presented as a generalization of the frequentist approach. The choice of probabilized the space of states is truly revolutionary. Statisticians draw a hermetic border between the notion of unknown parameter and random parameter. In this article, we want to show the interest of Bayesian econometrics and this analytical approach on three important points; in the level of characteristics, in the advantages and finally in the properties.</em>

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29

Hamimes, Ahmed, Rachid Benamirouche, and Fatih Chellai. "Bayesian methods in applied econometrics, oradvantages and properties of Bayesian statistics." Journal of Economic Growth and Entrepreneurship JEGE 5, no. 1 (2021): 1–10. https://doi.org/10.5281/zenodo.4468697.

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<strong><em>Bayesian statistics can be presented as a generalization of the frequentist approach. The choice of probabilized the space of states is truly revolutionary. Statisticians draw a hermetic border between the notion of unknown parameter and random parameter. In this article, we want to show the interest of Bayesian econometrics and this analytical approach on three important points; in the level of characteristics, in the advantages and finally in the properties</em></strong>

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30

Higginson, Andrew D., Tim W. Fawcett, Alasdair I. Houston, and John M. McNamara. "Trust your gut: using physiological states as a source of information is almost as effective as optimal Bayesian learning." Proceedings of the Royal Society B: Biological Sciences 285, no. 1871 (2018): 20172411. http://dx.doi.org/10.1098/rspb.2017.2411.

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Approaches to understanding adaptive behaviour often assume that animals have perfect information about environmental conditions or are capable of sophisticated learning. If such learning abilities are costly, however, natural selection will favour simpler mechanisms for controlling behaviour when faced with uncertain conditions. Here, we show that, in a foraging context, a strategy based only on current energy reserves often performs almost as well as a Bayesian learning strategy that integrates all previous experiences to form an optimal estimate of environmental conditions. We find that Bay

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31

Bernardi, Mauro, Stefano Grassi, and Francesco Ravazzolo. "Bayesian Econometrics." Journal of Risk and Financial Management 13, no. 11 (2020): 257. http://dx.doi.org/10.3390/jrfm13110257.

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The computational revolution in simulation techniques has shown to become a key ingredient in the field of Bayesian econometrics and opened new possibilities to study complex economic and financial phenomena. Applications include risk measurement, forecasting, assessment of policy effectiveness in macro, finance, marketing and monetary economics.

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32

Itagaki, Hiroshi, Hiroo Asada, and Seiichi Itoh. "Bayesian Estimation." Journal of the Society of Naval Architects of Japan 1985, no. 157 (1985): 285–94. http://dx.doi.org/10.2534/jjasnaoe1968.1985.285.

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33

Jackson, Mathew O. "Bayesian Implementation." Econometrica 59, no. 2 (1991): 461. http://dx.doi.org/10.2307/2938265.

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34

Dimitrakakis, Christos, Yang Liu, David C. Parkes, and Goran Radanovic. "Bayesian Fairness." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 509–16. http://dx.doi.org/10.1609/aaai.v33i01.3301509.

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We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in (Kleinberg, Mullainathan, and Raghavan 2016), we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.

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35

Ziegel, Eric R., D. Berry, and D. Stangl. "Bayesian Biostatistics." Technometrics 39, no. 1 (1997): 111. http://dx.doi.org/10.2307/1270800.

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36

Eilat, Ran, Kfir Eliaz, and Xiaosheng Mu. "Bayesian privacy." Theoretical Economics 16, no. 4 (2021): 1557–603. http://dx.doi.org/10.3982/te4390.

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Modern information technologies make it possible to store, analyze, and trade unprecedented amounts of detailed information about individuals. This has led to public discussions on whether individuals' privacy should be better protected by restricting the amount or the precision of information that is collected by commercial institutions on their participants. We contribute to this discussion by proposing a Bayesian approach to measure loss of privacy in a mechanism. Specifically, we define the loss of privacy associated with a mechanism as the difference between the designer's prior and poste

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37

Landes, Jürgen. "Bayesian Epistemology." KRITERION – Journal of Philosophy 36, no. 1 (2022): 1–7. http://dx.doi.org/10.1515/krt-2022-0005.

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38

Alencar, Alisson S. C., Cesar L. C. Mattos, Joao P. P. Gomes, and Diego Mesquita. "Bayesian Multilateration." IEEE Signal Processing Letters 29 (2022): 962–66. http://dx.doi.org/10.1109/lsp.2022.3161122.

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39

Zellner, Arnold. "Bayesian Econometrics." Econometrica 53, no. 2 (1985): 253. http://dx.doi.org/10.2307/1911235.

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40

MW, Nicholas G. Polson, and George C. Tiao. "Bayesian Inference." Journal of the American Statistical Association 91, no. 433 (1996): 441. http://dx.doi.org/10.2307/2291438.

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41

MTW, Donald A. Berry, and Dalene K. Stangl. "Bayesian Biostatistics." Journal of the American Statistical Association 91, no. 436 (1996): 1754. http://dx.doi.org/10.2307/2291615.

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42

Dickey, James M., Morris L. Eaton, J. M. Bernardo, and Adrian F. M. Smith. "Bayesian Theory." Journal of the American Statistical Association 91, no. 434 (1996): 906. http://dx.doi.org/10.2307/2291685.

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43

Fearn, T., D. A. Berry, and D. K. Stangl. "Bayesian Biostatistics." Biometrics 53, no. 4 (1997): 1560. http://dx.doi.org/10.2307/2533526.

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44

Lindley, Dennis. "Bayesian thoughts." Significance 1, no. 2 (2004): 73–75. http://dx.doi.org/10.1111/j.1740-9713.2004.027.x.

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45

Morey, Richard D. "“Bayesian Statistics”." Zeitschrift für Psychologie 223, no. 2 (2015): 145. http://dx.doi.org/10.1027/2151-2604/a000203.

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46

Ravi, Sreenivasan. "Bayesian Reliability." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (2010): 935. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_4.x.

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47

Puga, Jorge López, Martin Krzywinski, and Naomi Altman. "Bayesian statistics." Nature Methods 12, no. 5 (2015): 377–78. http://dx.doi.org/10.1038/nmeth.3368.

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48

Puga, Jorge López, Martin Krzywinski, and Naomi Altman. "Bayesian networks." Nature Methods 12, no. 9 (2015): 799–800. http://dx.doi.org/10.1038/nmeth.3550.

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49

Kamenica, Emir, and Matthew Gentzkow. "Bayesian Persuasion." American Economic Review 101, no. 6 (2011): 2590–615. http://dx.doi.org/10.1257/aer.101.6.2590.

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When is it possible for one person to persuade another to change her action? We consider a symmetric information model where a sender chooses a signal to reveal to a receiver, who then takes a noncontractible action that affects the welfare of both players. We derive necessary and sufficient conditions for the existence of a signal that strictly benefits the sender. We characterize sender-optimal signals. We examine comparative statics with respect to the alignment of the sender's and the receiver's preferences. Finally, we apply our results to persuasion by litigators, lobbyists, and salespeo

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50

Leonard, Thomas. "BAYESIAN THEORY." Bulletin of the London Mathematical Society 28, no. 6 (1996): 670–71. http://dx.doi.org/10.1112/blms/28.6.670.

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