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Journal articles on the topic 'Bayesian framework'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Dasgupta, Anirban, George Casella, Mohan Delampady, Christian Genest, William E. Strawderman, and Herman Rubin. "Correlation in a Bayesian framework." Canadian Journal of Statistics 28, no. 4 (December 2000): 675–87. http://dx.doi.org/10.2307/3315910.

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2

Zhang, Rui, and Ling Guan. "A Bayesian Image Retrieval Framework." International Journal of Digital Library Systems 1, no. 2 (2010): 43–58. http://dx.doi.org/10.4018/jdls.2010040103.

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3

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

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.

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4

Calvetti, Daniela, and Erkki Somersalo. "Hypermodels in the Bayesian imaging framework." Inverse Problems 24, no. 3 (May 23, 2008): 034013. http://dx.doi.org/10.1088/0266-5611/24/3/034013.

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5

Modrak, Ryan T., Stephen J. Arrowsmith, and Dale N. Anderson. "A Bayesian framework for infrasound location." Geophysical Journal International 181, no. 1 (April 2010): 399–405. http://dx.doi.org/10.1111/j.1365-246x.2010.04499.x.

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6

Grzywacz, Norberto M., and Rosario M. Balboa. "A Bayesian Framework for Sensory Adaptation." Neural Computation 14, no. 3 (March 1, 2002): 543–59. http://dx.doi.org/10.1162/089976602317250898.

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Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.

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7

Jbabdi, S., M. W. Woolrich, J. L. R. Andersson, and T. E. J. Behrens. "A Bayesian framework for global tractography." NeuroImage 37, no. 1 (August 2007): 116–29. http://dx.doi.org/10.1016/j.neuroimage.2007.04.039.

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8

Turkoz, Mehmet, Sangahn Kim, Young-Seon Jeong, Myong K. (MK) Jeong, Elsayed A. Elsayed, Khalifa N. Al-Khalifa, and Abdel Magid Hamouda. "Bayesian framework for fault variable identification." Journal of Quality Technology 51, no. 4 (October 30, 2018): 375–91. http://dx.doi.org/10.1080/00224065.2018.1507561.

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9

Calvetti, Daniela, Jari P. Kaipio, and Erkki Somersalo. "Inverse problems in the Bayesian framework." Inverse Problems 30, no. 11 (October 29, 2014): 110301. http://dx.doi.org/10.1088/0266-5611/30/11/110301.

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10

DelSole, Timothy. "A Bayesian Framework for Multimodel Regression." Journal of Climate 20, no. 12 (June 15, 2007): 2810–26. http://dx.doi.org/10.1175/jcli4179.1.

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Abstract This paper presents a framework based on Bayesian regression and constrained least squares methods for incorporating prior beliefs in a linear regression problem. Prior beliefs are essential in regression theory when the number of predictors is not a small fraction of the sample size, a situation that leads to overfitting—that is, to fitting variability due to sampling errors. Under suitable assumptions, both the Bayesian estimate and the constrained least squares solution reduce to standard ridge regression. New generalizations of ridge regression based on priors relevant to multimodel combinations also are presented. In all cases, the strength of the prior is measured by a parameter called the ridge parameter. A “two-deep” cross-validation procedure is used to select the optimal ridge parameter and estimate the prediction error. The proposed regression estimates are tested on the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) hindcasts of seasonal mean 2-m temperature over land. Surprisingly, none of the regression models proposed here can consistently beat the skill of a simple multimodel mean, despite the fact that one of the regression models recovers the multimodel mean in a suitable limit. This discrepancy arises from the fact that methods employed to select the ridge parameter are themselves sensitive to sampling errors. It is plausible that incorporating the prior belief that regression parameters are “large scale” can reduce overfitting and result in improved performance relative to the multimodel mean. Despite this, results from the multimodel mean demonstrate that seasonal mean 2-m temperature is predictable for at least three months in several regions.

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11

Maroulas, Vasileios, Farzana Nasrin, and Christopher Oballe. "A Bayesian Framework for Persistent Homology." SIAM Journal on Mathematics of Data Science 2, no. 1 (January 2020): 48–74. http://dx.doi.org/10.1137/19m1268719.

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12

Shikano, Susumu. "Hypothesis Testing in the Bayesian Framework." Swiss Political Science Review 25, no. 3 (September 2019): 288–99. http://dx.doi.org/10.1111/spsr.12375.

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13

Yet, Barbaros, Zane B. Perkins, Nigel R. M. Tai, and D. William R. Marsh. "Clinical evidence framework for Bayesian networks." Knowledge and Information Systems 50, no. 1 (March 22, 2016): 117–43. http://dx.doi.org/10.1007/s10115-016-0932-1.

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14

Weisberg, Jonathan. "Locating IBE in the Bayesian framework." Synthese 167, no. 1 (February 14, 2008): 125–43. http://dx.doi.org/10.1007/s11229-008-9305-y.

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15

MacKay, David J. C. "A Practical Bayesian Framework for Backpropagation Networks." Neural Computation 4, no. 3 (May 1992): 448–72. http://dx.doi.org/10.1162/neco.1992.4.3.448.

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A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules for network pruning or growing procedures, (3) objective choice of magnitude and type of weight decay terms or additive regularizers (for penalizing large weights, etc.), (4) a measure of the effective number of well-determined parameters in a model, (5) quantified estimates of the error bars on network parameters and on network output, and (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian "evidence" automatically embodies "Occam's razor," penalizing overflexible and overcomplex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalization ability and the Bayesian evidence is obtained.

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16

Lin, Xingbin, Qi Guo, Deyu Yuan, and Min Gao. "Bayesian Optimization Framework for HVAC System Control." Buildings 13, no. 2 (January 20, 2023): 314. http://dx.doi.org/10.3390/buildings13020314.

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The use of machine-learning algorithms in optimizing the energy efficiency of HVAC systems has been widely studied in recent years. Previous research has focused mainly on data-driven model predictive controls and reinforcement learning. Both approaches require a large amount of online interactive data; therefore, they are not efficient and stable enough for large-scale practical applications. In this paper, a Bayesian optimization framework for HVAC control has been proposed to achieve near-optimal control performance while also maintaining high efficiency and stability, which would allow it to be implemented in a large number of projects to obtain large-scale benefits. The proposed framework includes the following: (1) a method for modeling HVAC control problems as contexture Bayesian optimization problems and a technology for automatically constructing Bayesian optimization samples, which are based on time series raw trending data; (2) a Gaussian process regression surrogate model for the objective function of optimization; (3) a Bayesian optimization control loop, optimized for the characteristics of HVAC system controls, including an additional exploration trick based on noise estimation and a mechanism to ensure constraint satisfaction. The performance of the proposed framework was evaluated by using a simulation system, which was calibrated by using trending data from a real data center. The results of our study showed that the proposed approach achieved more than a 10% increase in energy-efficiency savings within a few weeks of optimization time compared with the original building automation control.

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17

Brown, Nathan P., Samuel J. Grauer, Jason A. Deibel, Mitchell L. R. Walker, and Adam M. Steinberg. "Bayesian framework for THz-TDS plasma diagnostics." Optics Express 29, no. 4 (February 1, 2021): 4887. http://dx.doi.org/10.1364/oe.417396.

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18

Gómez-Vargas, Isidro, Ricardo Medel Esquivel, Ricardo García-Salcedo, and J. Alberto Vázquez. "Neural network within a Bayesian inference framework." Journal of Physics: Conference Series 1723, no. 1 (January 1, 2021): 012022. http://dx.doi.org/10.1088/1742-6596/1723/1/012022.

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19

R. "Bayesian Framework in Repeated-Play Decision Making." American Journal of Applied Sciences 9, no. 4 (April 1, 2012): 609–14. http://dx.doi.org/10.3844/ajassp.2012.609.614.

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20

Zhang Yan, Li Zhoujun, Ma Dianfu, and Xiong Zenggang. "Learning to Rank with Bayesian Evidence Framework." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 8 (September 30, 2011): 290–98. http://dx.doi.org/10.4156/aiss.vol3.issue8.36.

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21

Zhang, Li-Feng, Song Yang, Xiongyao Xie, Bing Qi, Zhensheng Cao, Biao Zhou, and Junli Zhai. "Ground condition assessment based on Bayesian framework." IOP Conference Series: Earth and Environmental Science 861, no. 5 (October 1, 2021): 052075. http://dx.doi.org/10.1088/1755-1315/861/5/052075.

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22

Dasgupta, Sayan, Mia R. Moore, Dobromir T. Dimitrov, and James P. Hughes. "Bayesian validation framework for dynamic epidemic models." Epidemics 37 (December 2021): 100514. http://dx.doi.org/10.1016/j.epidem.2021.100514.

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23

Chandra, Rohitash, and Animesh Tiwari. "Distributed Bayesian optimisation framework for deep neuroevolution." Neurocomputing 470 (January 2022): 51–65. http://dx.doi.org/10.1016/j.neucom.2021.10.045.

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24

Kalidindi, Surya R. "A Bayesian framework for materials knowledge systems." MRS Communications 9, no. 02 (May 7, 2019): 518–31. http://dx.doi.org/10.1557/mrc.2019.56.

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25

Xu, Bingang. "BAYESIAN NETWORK FRAMEWORK FOR ROTOR FAULT DIAGNOSIS." Chinese Journal of Mechanical Engineering 40, no. 01 (2004): 66. http://dx.doi.org/10.3901/jme.2004.01.066.

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26

Tick, Jenni, Aki Pulkkinen, Felix Lucka, Robert Ellwood, Ben T. Cox, Jari P. Kaipio, Simon R. Arridge, and Tanja Tarvainen. "Three dimensional photoacoustic tomography in Bayesian framework." Journal of the Acoustical Society of America 144, no. 4 (October 2018): 2061–71. http://dx.doi.org/10.1121/1.5057109.

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27

Ferreira, J. F., J. Lobo, P. Bessiere, M. Castelo-Branco, and J. Dias. "A Bayesian framework for active artificial perception." IEEE Transactions on Cybernetics 43, no. 2 (April 2013): 699–711. http://dx.doi.org/10.1109/tsmcb.2012.2214477.

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28

Barrett, Enda, Jim Duggan, and Enda Howley. "A parallel framework for Bayesian reinforcement learning." Connection Science 26, no. 1 (January 2, 2014): 7–23. http://dx.doi.org/10.1080/09540091.2014.885268.

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29

Bergman, Bo. "Conceptualistic Pragmatism: A framework for Bayesian analysis?" IIE Transactions 41, no. 1 (November 7, 2008): 86–93. http://dx.doi.org/10.1080/07408170802322713.

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30

Pavlovic, V., A. Garg, and S. Kasif. "A Bayesian framework for combining gene predictions." Bioinformatics 18, no. 1 (January 1, 2002): 19–27. http://dx.doi.org/10.1093/bioinformatics/18.1.19.

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31

Henderson, Heath, and Lendie Follett. "A Bayesian framework for estimating human capabilities." World Development 129 (May 2020): 104872. http://dx.doi.org/10.1016/j.worlddev.2019.104872.

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32

Lee, Jihwan, and Yoo S. Hong. "Design freeze sequencing using Bayesian network framework." Industrial Management & Data Systems 115, no. 7 (August 10, 2015): 1204–24. http://dx.doi.org/10.1108/imds-03-2015-0095.

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Purpose – Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk. Design/methodology/approach – A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence. Findings – The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing. Originality/value – The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.

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33

Arhab, S., H. Ayasso, B. Duchêne, and A. Mohammad-Djafari. "Optical imaging in a variational Bayesian framework." Journal of Physics: Conference Series 542 (October 21, 2014): 012008. http://dx.doi.org/10.1088/1742-6596/542/1/012008.

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34

Turner, Mick, and Edwin R. Hancock. "A Bayesian framework for 3D surface estimation." Pattern Recognition 34, no. 4 (April 2001): 903–22. http://dx.doi.org/10.1016/s0031-3203(00)00025-x.

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35

McSherry, D. "Sequential diagnosis in the independence Bayesian framework." Soft Computing - A Fusion of Foundations, Methodologies and Applications 8, no. 2 (December 1, 2003): 118–25. http://dx.doi.org/10.1007/s00500-002-0252-0.

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36

Walker, Stephen G., and Eduardo Gutiérrez-Peña. "Bayesian parametric inference in a nonparametric framework." TEST 16, no. 1 (February 27, 2007): 188–97. http://dx.doi.org/10.1007/s11749-006-0008-8.

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37

Loya, H., D. Anand, P. Poduval, N. Kumar, and A. Sethi. "A Bayesian framework to quantify survival uncertainty." Annals of Oncology 30 (November 2019): vii32—vii33. http://dx.doi.org/10.1093/annonc/mdz413.116.

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38

Yao, Chen, Ci Wang, Lijuan Hong, and Yunfei Cheng. "A Bayesian Probabilistic Framework for Rain Detection." Entropy 16, no. 6 (June 17, 2014): 3302–14. http://dx.doi.org/10.3390/e16063302.

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39

Newton, Kit, Qin Li, and Andrew M. Stuart. "Diffusive Optical Tomography in the Bayesian Framework." Multiscale Modeling & Simulation 18, no. 2 (January 2020): 589–611. http://dx.doi.org/10.1137/19m1247346.

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40

Malik, Sarmad, Jacob Benesty, and Jingdong Chen. "A Bayesian Framework for Blind Adaptive Beamforming." IEEE Transactions on Signal Processing 62, no. 9 (May 2014): 2370–84. http://dx.doi.org/10.1109/tsp.2014.2310432.

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41

Zhang, J., L. M. Zhang, and Wilson H. Tang. "Bayesian Framework for Characterizing Geotechnical Model Uncertainty." Journal of Geotechnical and Geoenvironmental Engineering 135, no. 7 (July 2009): 932–40. http://dx.doi.org/10.1061/(asce)gt.1943-5606.0000018.

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42

Ovaskainen, Otso, José Manuel Cano, and Juha Merilä. "A Bayesian framework for comparative quantitative genetics." Proceedings of the Royal Society B: Biological Sciences 275, no. 1635 (January 23, 2008): 669–78. http://dx.doi.org/10.1098/rspb.2007.0949.

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43

Al-Najjar, Nabil I. "A Bayesian Framework for the Precautionary Principle." Journal of Legal Studies 44, S2 (June 2015): S337—S365. http://dx.doi.org/10.1086/684304.

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44

Yee, Kenton K. "A Bayesian framework for combining valuation estimates." Review of Quantitative Finance and Accounting 30, no. 3 (September 22, 2007): 339–54. http://dx.doi.org/10.1007/s11156-007-0055-6.

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45

Li, Yuting, and Fuyuan Xiao. "Bayesian Update with Information Quality under the Framework of Evidence Theory." Entropy 21, no. 1 (December 21, 2018): 5. http://dx.doi.org/10.3390/e21010005.

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Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster’s combination rule to obtain the fusion result. Finally, with the aid of pignistic probability transformation, the combination result is converted to posterior probability distribution. A numerical example and a real application in target recognition show the efficiency of the proposed method. The proposed method can be seen as the generalized Bayesian update. If the information quality is not considered, the proposed method degenerates to the classical Bayesian update.

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46

Spiegler, Ran. "Bayesian Networks and Boundedly Rational Expectations *." Quarterly Journal of Economics 131, no. 3 (March 7, 2016): 1243–90. http://dx.doi.org/10.1093/qje/qjw011.

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AbstractI present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). The DM is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p , resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian network formula. As a result of this belief distortion, the DM’s evaluation of actions can vary with their long-run frequencies. Accordingly, I define a ”personal equilibrium” notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM’s behavior. I demonstrate the framework’s scope of applications with examples covering diverse areas, from demand for education to public policy.

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47

Zammit-Mangion, Andrew, Michael Bertolacci, Jenny Fisher, Ann Stavert, Matthew Rigby, Yi Cao, and Noel Cressie. "WOMBAT v1.0: a fully Bayesian global flux-inversion framework." Geoscientific Model Development 15, no. 1 (January 6, 2022): 45–73. http://dx.doi.org/10.5194/gmd-15-45-2022.

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Abstract. WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

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48

Maoying, Qiao, Yu Jun, Liu Tongliang, Wang Xinchao, and Tao Dacheng. "Diversified Bayesian Nonnegative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5420–27. http://dx.doi.org/10.1609/aaai.v34i04.5991.

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Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data. In this paper, we approach this issue using a Bayesian framework. We propose to assign a diversity prior to the parts of the factorization to induce correctness based on the assumption that useful parts should be distinct and thus well-spread. A Bayesian framework including this diversity prior is then established. This framework aims at inducing factorizations embracing both good data fitness from maximizing likelihood and large separability from the diversity prior. Specifically, the diversity prior is formulated with determinantal point processes (DPP) and is seamlessly embedded into a Bayesian NMF framework. To carry out the inference, a Monte Carlo Markov Chain (MCMC) based procedure is derived. Experiments conducted on a synthetic dataset and a real-world MULAN dataset for multi-label learning (MLL) task demonstrate the superiority of the proposed method.

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49

Moghaddass, Ramin, and Shuangwen Sheng. "An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework." Applied Energy 240 (April 2019): 561–82. http://dx.doi.org/10.1016/j.apenergy.2019.02.025.

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50

Wang, Jingsong, and Marco Valtorta. "A Framework for Integration of Logical and Probabilistic Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1822–23. http://dx.doi.org/10.1609/aaai.v25i1.8048.

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Integrating the expressive power of first-order logic with the ability of probabilistic reasoning of Bayesian networks has attracted the interest of many researchers for decades. We present an approach to integration that translates logical knowledge into Bayesian networks and uses Bayesian network composition to build a uniform representation that supports both logical and probabilistic reasoning. In particular, we propose a new way of translation of logical knowledge, relation search. Through the use of the proposed framework, without learning new languages or tools, modelers are allowed to 1) specify special knowledge using the most suitable languages, while reasoning in a uniform engine; 2) make use of pre-existing logical knowledge bases for probabilistic reasoning (to complete the model or minimize potential inconsistencies).

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