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Artykuły w czasopismach na temat „Bayesian rules”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Howson, Colin. "Bayesian rules of updating." Erkenntnis 45, no. 2-3 (1996): 195–208. http://dx.doi.org/10.1007/bf00276790.

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2

Nicholl, Jon, and Steve Goodacre. "Bayesian stopping rules for trials." Lancet 359, no. 9300 (2002): 76. http://dx.doi.org/10.1016/s0140-6736(02)07292-6.

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3

Parmar, Mahesh KB, David J. Spiegelhalter, Gareth O. Griffiths, Douglas G. Altman, and Robert L. Souhami. "Bayesian stopping rules for trials." Lancet 359, no. 9300 (2002): 76–77. http://dx.doi.org/10.1016/s0140-6736(02)07293-8.

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4

Kulvichit, Kittisak. "Bayesian stopping rules for trials." Lancet 359, no. 9300 (2002): 77. http://dx.doi.org/10.1016/s0140-6736(02)07294-x.

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5

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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6

Long, Yuqi, and Xingzhong Xu. "Bayesian decision rules to classification problems." Australian & New Zealand Journal of Statistics 63, no. 2 (2021): 394–415. http://dx.doi.org/10.1111/anzs.12325.

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7

Majumdar, Dipjyoti, and Arunava Sen. "Ordinally Bayesian Incentive Compatible Voting Rules." Econometrica 72, no. 2 (2004): 523–40. http://dx.doi.org/10.1111/j.1468-0262.2004.00499.x.

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8

Ankirchner, Stefan, and Maike Klein. "Bayesian sequential testing with expectation constraints." ESAIM: Control, Optimisation and Calculus of Variations 26 (2020): 51. http://dx.doi.org/10.1051/cocv/2019045.

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We study a stopping problem arising from a sequential testing of two simple hypotheses H0 and H1 on the drift rate of a Brownian motion. We impose an expectation constraint on the stopping rules allowed and show that an optimal stopping rule satisfying the constraint can be found among the rules of the following type: stop if the posterior probability for H1 attains a given lower or upper barrier; or stop if the posterior probability comes back to a fixed intermediate point after a sufficiently large excursion. Stopping at the intermediate point means that the testing is abandoned without acce

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9

Gubler, Philipp, and Makoto Oka. "QCD sum rules in a Bayesian approach." Journal of Physics: Conference Series 312, no. 3 (2011): 032008. http://dx.doi.org/10.1088/1742-6596/312/3/032008.

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10

van Enk, Steven J. "Bayesian Measures of Confirmation from Scoring Rules." Philosophy of Science 81, no. 1 (2014): 101–13. http://dx.doi.org/10.1086/674205.

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11

Gubler, P., and M. Oka. "A Bayesian Approach to QCD Sum Rules." Progress of Theoretical Physics 124, no. 6 (2010): 995–1018. http://dx.doi.org/10.1143/ptp.124.995.

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12

Giummolè, F., V. Mameli, E. Ruli, and L. Ventura. "Objective Bayesian inference with proper scoring rules." TEST 28, no. 3 (2018): 728–55. http://dx.doi.org/10.1007/s11749-018-0597-z.

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13

Alcalá-Quintana, Rocío, and Miguel García-pérez. "Stopping rules in Bayesian adaptive threshold estimation." Spatial Vision 18, no. 3 (2005): 347–74. http://dx.doi.org/10.1163/1568568054089375.

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14

Monahan, John, and Alan Genz. "Spherical-Radial Integration Rules for Bayesian Computation." Journal of the American Statistical Association 92, no. 438 (1997): 664–74. http://dx.doi.org/10.1080/01621459.1997.10474018.

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15

Orsenigo, Carlotta, and Carlo Vercellis. "Bayesian Stopping Rules for Greedy Randomized Procedures." Journal of Global Optimization 36, no. 3 (2006): 365–77. http://dx.doi.org/10.1007/s10898-006-9014-3.

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16

Basu, Pathikrit. "Bayesian updating rules and AGM belief revision." Journal of Economic Theory 179 (January 2019): 455–75. http://dx.doi.org/10.1016/j.jet.2018.11.005.

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17

Aminian, Minoo, David Couvin, Amina Shabbeer, et al. "PredictingMycobacterium tuberculosisComplex Clades Using Knowledge-Based Bayesian Networks." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/398484.

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We develop a novel approach for incorporating expert rules into Bayesian networks for classification ofMycobacterium tuberculosiscomplex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades foun

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18

Lopes, Lola L. "Averaging rules and adjustment processes in Bayesian inference." Bulletin of the Psychonomic Society 23, no. 6 (1985): 509–12. http://dx.doi.org/10.3758/bf03329868.

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19

Martins, André C. R. "Bayesian updating rules in continuous opinion dynamics models." Journal of Statistical Mechanics: Theory and Experiment 2009, no. 02 (2009): P02017. http://dx.doi.org/10.1088/1742-5468/2009/02/p02017.

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20

Dawid, A. Philip, and Monica Musio. "Bayesian Model Selection Based on Proper Scoring Rules." Bayesian Analysis 10, no. 2 (2015): 479–99. http://dx.doi.org/10.1214/15-ba942.

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21

Callen, Craig R. "Cognitive Science, Bayesian Norms and Rules of Evidence." Journal of the Royal Statistical Society. Series A (Statistics in Society) 154, no. 1 (1991): 129. http://dx.doi.org/10.2307/2982706.

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22

Boender, C. G. E., and A. H. G. Rinnooy Kan. "Bayesian stopping rules for multistart global optimization methods." Mathematical Programming 37, no. 1 (1987): 59–80. http://dx.doi.org/10.1007/bf02591684.

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23

Intan, Rolly, Oviliani Yenty Yuliana, and Dwi Kristanto. "Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules." Jurnal Teknik Industri 12, no. 1 (2010): 55–60. http://dx.doi.org/10.9744/jti.12.1.55-60.

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Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implement

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24

DEMİR, İbrahim, Ersin ŞENER, Hasan Aykut KARABOĞA, and Ahmet BAŞAL. "Expectations of Students from Classroom Rules: A Scenario Based Bayesian Network Analysis." Participatory Educational Research 10, no. 1 (2023): 424–42. http://dx.doi.org/10.17275/per.23.23.10.1.

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Classroom rules are a fundamental aspect of classroom management and ensuring compliance with established rules is crucial. Previous research has shown that students often pay little attention to the development of classroom rules. This quantitative study aims to investigate the expectations that students have concerning classroom rules. To this end, a 4-point Likert scale questionnaire consisting of 30 items was administered to 356 secondary school students. The Bayesian Search method and expert opinion were used to obtain a Bayesian Network model. The findings of the study indicate that stud

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25

Xu, Shi Jun, Li Hong, and Yong Hong Hu. "A Distributed Bayesian Fusion Algorithm Research." Advanced Materials Research 181-182 (January 2011): 1006–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1006.

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In this paper, the signal detection problem when distributed sensors are used a global decision is desired is considered. Local decisions from the sensors are fed to the data fusion center which then yields a global decision based on a fusion rule. Based on The data fusion theories of Bayesian criterion used for a distributed parallel structure, fusion rules at the fusion center、 the decision rules of sensors and the results of the computer simulation for two identical sensors, two different sensors and three identical sensors are presented. The results of the computer simulation show that the

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26

Kim, Jinsub. "On Optimality of Deterministic Rules in Adversarial Bayesian Detection." IEEE Signal Processing Letters 29 (2022): 757–61. http://dx.doi.org/10.1109/lsp.2022.3140694.

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27

Belforte, Gustavo, Basilio Bona, and Roberto Tempo. "Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 4 (1986): 502–11. http://dx.doi.org/10.1109/tpami.1986.4767814.

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28

Järvenpää, Marko, Michael U. Gutmann, Arijus Pleska, Aki Vehtari, and Pekka Marttinen. "Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation." Bayesian Analysis 14, no. 2 (2019): 595–622. http://dx.doi.org/10.1214/18-ba1121.

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29

van den Bos, L. M. M., B. Sanderse, and W. A. A. M. Bierbooms. "Adaptive sampling-based quadrature rules for efficient Bayesian prediction." Journal of Computational Physics 417 (September 2020): 109537. http://dx.doi.org/10.1016/j.jcp.2020.109537.

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30

Mookherjee, Dilip, and Stefan Reichelstein. "Dominant strategy implementation of Bayesian incentive compatible allocation rules." Journal of Economic Theory 56, no. 2 (1992): 378–99. http://dx.doi.org/10.1016/0022-0531(92)90088-y.

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31

Cools, Ronald, and Petros Dellaportas. "The role of embedded integration rules in Bayesian statistics." Statistics and Computing 6, no. 3 (1996): 245–50. http://dx.doi.org/10.1007/bf00140868.

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32

Dayanik, Savas, Warren B. Powell, and Kazutoshi Yamazaki. "Asymptotically optimal Bayesian sequential change detection and identification rules." Annals of Operations Research 208, no. 1 (2012): 337–70. http://dx.doi.org/10.1007/s10479-012-1121-6.

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33

Woike, Jan K., Ralph Hertwig, and Gerd Gigerenzer. "Heterogeneity of rules in Bayesian reasoning: A toolbox analysis." Cognitive Psychology 143 (June 2023): 101564. http://dx.doi.org/10.1016/j.cogpsych.2023.101564.

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34

Luo, X. G., L. Zhou, and R. Wang. "Manufacturing Process Based on Recognition Rules and Bayesian Networks." International Journal of Simulation Modelling 24, no. 1 (2025): 135–46. https://doi.org/10.2507/ijsimm24-1-co2.

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35

Wang, Yanmin, and Jing Jin. "Research on Bayesian Statistics Teaching with R." International Journal of Education and Humanities 12, no. 1 (2024): 39–40. http://dx.doi.org/10.54097/5zh7f436.

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Bayesian statistics uses the mathematical rules of probability to combine data with prior information to yield inferences which are always more precise than would be obtained by either source of information alone. The current Bayesian education has not yet attracted enough attention from the education community in China. The rise of MCMC and probabilistic programming languages has profoundly reshaped Bayesian statistics. Compared to precise mathematical analysis, the simulation-based teaching can help students to avoid tedious statistical calculations and cultivate Bayesian thinking.

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36

Schnabel, Georg. "Practicalities of Bayesian network modeling for nuclear data evaluation with the nucdataBaynet package." EPJ Web of Conferences 281 (2023): 00019. http://dx.doi.org/10.1051/epjconf/202328100019.

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Bayesian networks are a helpful abstraction in the modelization of the relationships between different variables for the purpose of uncertainty quantification. They are therefore especially well suited for the application to nuclear data evaluation to accurately model the relationships of experimental and nuclear models. Constraints, such as sum rules and the non-negativity of cross sections, can be rigorously taken into account in Bayesian inference within Bayesian networks. This contribution elaborates on the practical aspects of the construction of Bayesian networks with the nucdataBaynet p

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37

Zhao, Chen. "Pseudo‐Bayesian updating." Theoretical Economics 17, no. 1 (2022): 253–89. http://dx.doi.org/10.3982/te4535.

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I propose an axiomatic framework for belief revision when new information is qualitative, of the form “event A is at least as likely as event B.” My decision maker need not have beliefs about the joint distribution of the signal she will receive and the payoff‐relevant states. I propose three axioms, Exchangeability, Stationarity, and Reduction, to characterize the class of pseudo‐Bayesian updating rules. The key axiom, Exchangeability, requires that the order in which the information arrives does not matter if the different pieces of information neither reinforce nor contradict each other. I

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38

Vos, Hans J. "Applications of Bayesian Decision Theory to Sequential Mastery Testing." Journal of Educational and Behavioral Statistics 24, no. 3 (1999): 271–92. http://dx.doi.org/10.3102/10769986024003271.

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The purpose of this paper is to formulate optimal sequential rules for mastery tests. The framework for the approach is derived from Bayesian sequential decision theory. Both a threshold and linear loss structure are considered. The binomial probability distribution is adopted as the psychometric model involved. Conditions sufficient for sequentially setting optimal cutting scores are presented. Optimal sequential rules will be derived for the case of a subjective beta distribution representing prior true level of functioning. An empirical example of sequential mastery esting for concept-learn

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39

Zhao, Wen Qing, Yan Fang Zhang, and Sheng Long Zhang. "Bayesian Network with Association Rules Applied in the Recognition of Handwritten Digits." Advanced Materials Research 187 (February 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.187.7.

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Classification Based on Association (CBA) algorithm built a classifier based on the association rules, but without considering the uncertainty in the classification problem. This paper proposed a Bayesian network classifier based on the association rules. The algorithm extracts the candidate set uses association rules and classification algorithms related to the network, then uses “greedy hill-climbing algorithm” to learn network structure to get a better topology, and verify that this algorithm is valid on handwritten numeral recognition.

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40

Storari, Sergio, Fabrizio Riguzzi, and Evelina Lamma. "Exploiting association and correlation rules parameters for learning Bayesian networks." Intelligent Data Analysis 13, no. 5 (2009): 689–701. http://dx.doi.org/10.3233/ida-2009-0388.

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41

Rao, Wei, Lipeng Zhu, Sen Pan, Pei Yang, and Junfeng Qiao. "Bayesian Network and Association Rules-based Transformer Oil Temperature Prediction." Journal of Physics: Conference Series 1314 (October 2019): 012066. http://dx.doi.org/10.1088/1742-6596/1314/1/012066.

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42

Mann, Richard P. "Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups." PLoS ONE 6, no. 8 (2011): e22827. http://dx.doi.org/10.1371/journal.pone.0022827.

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43

Ruggeri, Fabrizio. "On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions." Econometric Reviews 33, no. 1-4 (2013): 289–304. http://dx.doi.org/10.1080/07474938.2013.807183.

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44

Tillman, Gabriel, and Nathan J. Evans. "Hierarchical Bayesian mixture models of processing architectures and stopping rules." Journal of Mathematical Psychology 92 (October 2019): 102267. http://dx.doi.org/10.1016/j.jmp.2019.04.005.

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45

Caraiani, Petre. "Comparing monetary policy rules in CEE economies: A Bayesian approach." Economic Modelling 32 (May 2013): 233–46. http://dx.doi.org/10.1016/j.econmod.2013.01.045.

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46

Jinhua, Wang, Ma Xuehua, and Cao Jie. "Fault diagnosis method of Bayesian network based on association rules." Transactions of the Institute of Measurement and Control 47, no. 9 (2025): 1906–14. https://doi.org/10.1177/01423312241269710.

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When the number of samples is large, the scale of the Bayesian network (BN) structure search space increases exponentially with the number of nodes, resulting in a sharp increase in the difficulty of learning the BN structure. Aiming at this problem, this paper proposes a fault diagnosis model construction method combining association rules and a BN network. The Euclidean distance under the Symbolic Aggregation Approximation (SAX) algorithm is utilized to compute and average the distance between the standard and faulty samples and filter the candidate nodes by the average value, which in turn

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47

Shi, Juan. "System Evaluation and Management of College Students’ Physical Exercise Behavior Stages Integrating Bayesian Association Rules Data Mining Algorithm." Advances in Multimedia 2022 (May 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/1655605.

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The form of association rules is simple, and it is efficient and convenient to apply. However, because association rules cannot express the connection between different rules, in some more complex application fields, when it is necessary to comprehensively consider the impact of multiple factors on the results, the application of rules is more difficult. In the process of reasoning about the node state, the influence of various factors (parent nodes) can be comprehensively considered. This paper proposes a Bayesian network-based association rule representation method. After mining the associat

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48

Li, Naiyi, Yongming Li, and Juan Huang. "Empirical Bayes Likelihood for Exponential Distribution Family under Ranked Set Sampling and Dependent Data." Academic Journal of Science and Technology 13, no. 1 (2024): 207–9. http://dx.doi.org/10.54097/4vj4sr10.

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In this paper, we get the empirical Bayes likelihood test rules for Exponential distribution based on ranked set sampling. Its asymptotic optimality is obtained. Due to the advantages of combining empirical Bayesian methods with prior information of samples, the accuracy of statistical inference can be improved. Therefore, the fusion of empirical Bayesian likelihood methods can further enhance the estimation methods of statistical models.

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49

Alfassa, Achmad Isya. "Bayesian Statistics for Study Population Statistics and Demography." Journal of Statistical Methods and Data Science 1, no. 1 (2023): 17–24. http://dx.doi.org/10.31258/jsmds.v1i1.4.

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Bayesian statistics is a method that belongs to the realm of statistical science which is based on the rules of the science of chance or probability. The Bayesian method is also used in carrying out projection analysis to see a picture of future conditions. This research was conducted to show the relationship between Bayesian Statistics and Demographic and Population Statistics Studies. The results of Bayesian Statistics can be used in the study of Population Statistics and Demography to carry out analysis with previous data and to find out and predict a picture of future conditions to determi

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50

Lo, Benjamin W. Y., R. Loch Macdonald, Andrew Baker, and Mitchell A. H. Levine. "Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/904860.

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Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH).Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients).Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in B

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