Artículos de revistas: "Bayesian"

1

Hutchon, David J. R. "Why clinicians are natural bayesians: Bayesian confusion". BMJ 330, n.º 7504 (9 de junio de 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, n.º 2 (6 de agosto de 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. y Rangarajan K. Sundaram. "Bayesian economists … Bayesian agents". Journal of Economic Dynamics and Control 17, n.º 3 (mayo de 1993): 355–83. http://dx.doi.org/10.1016/0165-1889(93)90002-a.

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4

Hicks, Tyler, Liliana Rodríguez-Campos y Jeong Hoon Choi. "Bayesian Posterior Odds Ratios". American Journal of Evaluation 39, n.º 2 (23 de mayo de 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 y William H. Starbuck. "Bayesian Studies: Why We All Should Be Bayesians". Academy of Management Proceedings 2018, n.º 1 (agosto de 2018): 18255. http://dx.doi.org/10.5465/ambpp.2018.18255symposium.

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6

Krackhardt, David, Andreas Schwab y William H. Starbuck. "Bayesian Statistics: Why We All Should Be Bayesians". Academy of Management Proceedings 2017, n.º 1 (agosto de 2017): 15147. http://dx.doi.org/10.5465/ambpp.2017.15147symposium.

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7

SHEARER, Robert y William SHEARER. "THE BAYESIAN ANTINOMY RESOLVED". International Journal of Theology, Philosophy and Science 3, n.º 5 (20 de noviembre de 2019): 5–11. http://dx.doi.org/10.26520/ijtps.2019.3.5.5-11.

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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, n.º 1 (3 de octubre de 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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9

Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN". PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, n.º 2 (1 de junio de 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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10

RIZAL, MUHAMMAD y 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, n.º 1 (29 de diciembre de 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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11

Withers, Suzanne Davies. "Quantitative methods: Bayesian inference, Bayesian thinking". Progress in Human Geography 26, n.º 4 (agosto de 2002): 553–66. http://dx.doi.org/10.1191/0309132502ph386pr.

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12

Fienberg, Stephen E. "When did Bayesian inference become "Bayesian"?" Bayesian Analysis 1, n.º 1 (marzo de 2006): 1–40. http://dx.doi.org/10.1214/06-ba101.

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13

Kyburg, Henry E. "Bayesian and non-bayesian evidential updating". Artificial Intelligence 31, n.º 3 (marzo de 1987): 271–93. http://dx.doi.org/10.1016/0004-3702(87)90068-3.

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14

Shim, Heejung y Bret Larget. "BayesCAT: Bayesian co-estimation of alignment and tree". Biometrics 74, n.º 1 (18 de enero de 2017): 270–79. http://dx.doi.org/10.1111/biom.12640.

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15

Na, Jonghyun, Taekseon Ryu, Joonmyoung Kim, Hansuk Kim, Manjae Kwon y Yongsung Joo. "A Bayesian Spatial Contamination Model". Korean Data Analysis Society 24, n.º 3 (30 de junio de 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 over the Bayesian contamination model in terms of biases and RMSE of estimators of the logistic regression parameters. We applied the proposed model into environmental data and confirmed the improvement on the model fit. Also, the clustering was reasonably performed from the environmental perspective, which was coherent with the fact that the underground water flows from the southwest side to the northeast side. This model is expected to be utilized effectively to monitor the quality of a ground or groundwater and capture the heterogeneity in it which is suspected of environmental pollution especially when the interested site consists of areas with strong spatial dependency.

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16

Mansour, Yishay, Alex Slivkins, Vasilis Syrgkanis y Zhiwei Steven Wu. "Bayesian Exploration: Incentivizing Exploration in Bayesian Games". Operations Research 70, n.º 2 (marzo de 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 recommendation policy can be modeled as a multiarmed bandit algorithm under Bayesian incentivecompatibility (BIC) constraints. This line of work has received considerable attention in the “economics and computation” community. Although in prior work, the planner interacts with a single agent at a time, the present paper allows the agents to affect one another directly in a shared environment. The agents now face two sources of uncertainty: what is the environment, and what would the other agents do? We focus on “explorable” actions: those that can be recommended by some BIC policy. We show how the principal can identify and explore all such actions.

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17

Gao, Xiaoguang, Yu Yang y Zhigao Gao. "Learning Bayesian networks by constrained Bayesian estimation". Journal of Systems Engineering and Electronics 30, n.º 03 (25 de junio de 2019): 511–24. http://dx.doi.org/10.21629/jsee.2019.03.09.

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18

Gopnik, Alison y Joshua B. Tenenbaum. "Bayesian networks, Bayesian learning and cognitive development". Developmental Science 10, n.º 3 (mayo de 2007): 281–87. http://dx.doi.org/10.1111/j.1467-7687.2007.00584.x.

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19

Lecouteux, Guilhem. "Bayesian game theorists and non-Bayesian players". European Journal of the History of Economic Thought 25, n.º 6 (2 de noviembre de 2018): 1420–54. http://dx.doi.org/10.1080/09672567.2018.1523207.

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20

Forbes, Florence y Adrian E. Raftery. "Bayesian Morphology: Fast Unsupervised Bayesian Image Analysis". Journal of the American Statistical Association 94, n.º 446 (junio de 1999): 555–68. http://dx.doi.org/10.1080/01621459.1999.10474150.

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21

Reyad, Hesham, Adil Mousa Younis y 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, n.º 1 (3 de abril de 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 of mean square error (MSE) via Monte Carlo simulation.</p>

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22

Poirier, Dale J. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics". Journal of Economic Perspectives 2, n.º 1 (1 de febrero de 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 occurrence of an event as the number of suitably defined trials goes to infinity. For a subjectivist, probability is interpreted as a degree of belief fundamentally internal to the individual as opposed to some characteristic of the external world. Subjective probability measures a relationship between the observer and events (not necessarily “repetitive”) of the outside world, expressing the observer's personal uncertainty about those events. The subjectivist paradigm is designed to produce “coherent” revisions in beliefs about future observables in light of observed data. Most of the issues I raise are familiar to statisticians but not to economists. Rather than give the suspicious reader a menu of Bayesian techniques, I hope to create an interest in acquiring a taste for the Bayesian cuisine by recommending five pragmatic principles.

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23

DEL ÁGUILA, ISABEL MARÍA y JOSÉ DEL SAGRADO. "REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS". International Journal of Software Engineering and Knowledge Engineering 21, n.º 02 (marzo de 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 performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.

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24

DAMODARAN, D., B. RAVIKUMAR y 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, n.º 06 (diciembre de 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 model for computer software, Appl. Stat. 22 (1973) 332–346] and they modeled hazard rate as a random variable. In this paper, a new Bayesian software reliability model is proposed by combining two prior distributions for predicting the total number of failures and the next failure time of the software. The popular and realistic Jelinski and Moranda (J&M) model is taken as a base for bringing out this model by applying Bayesian approach. It is assumed that one of the parameters of JM model N, number of faults in the software follows uniform prior distribution and another failure rate parameter Φi follows gama prior distribution. The joint prior p(N, Φi) is obtained by combining the above two prior distributions. In this Bayesian model, the time between failures follow exponential distribution with failure rate parameter with stochastically decreasing order on successive failure time intervals. The reasoning for the assumption on the parameter is that the intention of the software tester to improve the software quality by the correction of each failure. With Bayesian approach, the predictive distribution has been arrived at by combining exponential Time between Failures (TBFs) and joint prior p(N, Φi). For the parameter estimation, maximum likelihood estimation (MLE) method has been adopted. The proposed Bayesian software reliability model has been applied to two sets of act. The proposed model has been applied to two sets of actual software failure data and it has been observed that the predicted failure times as per the proposed model are closer to the actual failure times. The predicted failure times based on Littlewood–Verall (LV) model is also computed. Sum of square errors (SSE) criteria has been used for comparing the actual time between failures and predicted time between failures based on proposed model and LV model.

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25

Peters, Sunday O., Kadir Kızılkaya, Mahmut Sinecen, Burcu Mestav, Aranganoor K. Thiruvenkadan y Milton G. Thomas. "Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population". Animals 13, n.º 7 (6 de abril de 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 Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.

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26

Bernardi, Mauro, Stefano Grassi y Francesco Ravazzolo. "Bayesian Econometrics". Journal of Risk and Financial Management 13, n.º 11 (29 de octubre de 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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27

Itagaki, Hiroshi, Hiroo Asada y Seiichi Itoh. "Bayesian Estimation". Journal of the Society of Naval Architects of Japan 1985, n.º 157 (1985): 285–94. http://dx.doi.org/10.2534/jjasnaoe1968.1985.285.

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28

Jackson, Mathew O. "Bayesian Implementation". Econometrica 59, n.º 2 (marzo de 1991): 461. http://dx.doi.org/10.2307/2938265.

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29

Dimitrakakis, Christos, Yang Liu, David C. Parkes y Goran Radanovic. "Bayesian Fairness". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 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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30

Ziegel, Eric R., D. Berry y D. Stangl. "Bayesian Biostatistics". Technometrics 39, n.º 1 (febrero de 1997): 111. http://dx.doi.org/10.2307/1270800.

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31

Eilat, Ran, Kfir Eliaz y Xiaosheng Mu. "Bayesian privacy". Theoretical Economics 16, n.º 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 posterior beliefs about an agent's type, where this difference is calculated using Kullback–Leibler divergence, and where the change in beliefs is triggered by actions taken by the agent in the mechanism. We consider both ex post (for every realized type, the maximal difference in beliefs cannot exceed some threshold κ) and ex ante (the expected difference in beliefs over all type realizations cannot exceed some threshold κ) measures of privacy loss. Applying these notions to the monopolistic screening environment of Mussa and Rosen (1978), we study the properties of optimal privacy‐constrained mechanisms and the relation between welfare/profits and privacy levels.

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32

Landes, Jürgen. "Bayesian Epistemology". KRITERION – Journal of Philosophy 36, n.º 1 (17 de febrero de 2022): 1–7. http://dx.doi.org/10.1515/krt-2022-0005.

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33

Alencar, Alisson S. C., Cesar L. C. Mattos, Joao P. P. Gomes y 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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34

Zellner, Arnold. "Bayesian Econometrics". Econometrica 53, n.º 2 (marzo de 1985): 253. http://dx.doi.org/10.2307/1911235.

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35

MW, Nicholas G. Polson y George C. Tiao. "Bayesian Inference." Journal of the American Statistical Association 91, n.º 433 (marzo de 1996): 441. http://dx.doi.org/10.2307/2291438.

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MTW, Donald A. Berry y Dalene K. Stangl. "Bayesian Biostatistics." Journal of the American Statistical Association 91, n.º 436 (diciembre de 1996): 1754. http://dx.doi.org/10.2307/2291615.

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37

Dickey, James M., Morris L. Eaton, J. M. Bernardo y Adrian F. M. Smith. "Bayesian Theory." Journal of the American Statistical Association 91, n.º 434 (junio de 1996): 906. http://dx.doi.org/10.2307/2291685.

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38

Fearn, T., D. A. Berry y D. K. Stangl. "Bayesian Biostatistics." Biometrics 53, n.º 4 (diciembre de 1997): 1560. http://dx.doi.org/10.2307/2533526.

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39

Lindley, Dennis. "Bayesian thoughts". Significance 1, n.º 2 (26 de mayo de 2004): 73–75. http://dx.doi.org/10.1111/j.1740-9713.2004.027.x.

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40

Morey, Richard D. "“Bayesian Statistics”". Zeitschrift für Psychologie 223, n.º 2 (10 de julio de 2015): 145. http://dx.doi.org/10.1027/2151-2604/a000203.

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41

Ravi, Sreenivasan. "Bayesian Reliability". Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, n.º 4 (20 de septiembre de 2010): 935. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_4.x.

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42

Puga, Jorge López, Martin Krzywinski y Naomi Altman. "Bayesian statistics". Nature Methods 12, n.º 5 (29 de abril de 2015): 377–78. http://dx.doi.org/10.1038/nmeth.3368.

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Puga, Jorge López, Martin Krzywinski y Naomi Altman. "Bayesian networks". Nature Methods 12, n.º 9 (28 de agosto de 2015): 799–800. http://dx.doi.org/10.1038/nmeth.3550.

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44

Kamenica, Emir y Matthew Gentzkow. "Bayesian Persuasion". American Economic Review 101, n.º 6 (1 de octubre de 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 salespeople. (JEL D72, D82, D83, K40, M31)

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45

Leonard, Thomas. "BAYESIAN THEORY". Bulletin of the London Mathematical Society 28, n.º 6 (noviembre de 1996): 670–71. http://dx.doi.org/10.1112/blms/28.6.670.

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46

Seaman, John W. "Bayesian Methods". Technometrics 43, n.º 1 (febrero de 2001): 100. http://dx.doi.org/10.1198/tech.2001.s551.

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47

Banerjee, Sudipto y Alan E. Gelfand. "Bayesian Wombling". Journal of the American Statistical Association 101, n.º 476 (1 de diciembre de 2006): 1487–501. http://dx.doi.org/10.1198/016214506000000041.

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48

Epifani, Ilenia. "Bayesian Nonparametrics". Journal of the American Statistical Association 99, n.º 467 (septiembre de 2004): 898–99. http://dx.doi.org/10.1198/jasa.2004.s346.

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49

Elga, Adam. "Bayesian Humility". Philosophy of Science 83, n.º 3 (julio de 2016): 305–23. http://dx.doi.org/10.1086/685740.

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50

Belot, Gordon. "Bayesian Orgulity". Philosophy of Science 80, n.º 4 (octubre de 2013): 483–503. http://dx.doi.org/10.1086/673249.

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Artículos de revistas sobre el tema "Bayesian"

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