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Journal articles on the topic 'Objective Baye'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Held, Leonhard. "An objective Bayes perspective onp-values." Biometrical Journal 59, no. 5 (May 24, 2017): 886–88. http://dx.doi.org/10.1002/bimj.201700068.

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2

Villa, Cristiano, and Stephen G. Walker. "An objective Bayes factor with improper priors." Computational Statistics & Data Analysis 168 (April 2022): 107404. http://dx.doi.org/10.1016/j.csda.2021.107404.

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3

Hoijtink, Herbert. "Objective Bayes Factors for Inequality Constrained Hypotheses." International Statistical Review 81, no. 2 (June 13, 2013): 207–29. http://dx.doi.org/10.1111/insr.12010.

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4

Leon-Novelo, Luis, Elías Moreno, and George Casella. "Objective Bayes model selection in probit models." Statistics in Medicine 31, no. 4 (December 12, 2011): 353–65. http://dx.doi.org/10.1002/sim.4406.

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5

Daniel, J. Sudhir Ryan, and Chandrasekharan Rajendran. "Heuristic approaches to determine base-stock levels in a serial supply chain with a single objective and with multiple objectives." European Journal of Operational Research 175, no. 1 (November 2006): 566–92. http://dx.doi.org/10.1016/j.ejor.2005.04.039.

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6

Wei, Xin. "Multi-Objective Optimization Base on Incremental Pareto Fitness." Advanced Materials Research 1030-1032 (September 2014): 1733–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1733.

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A new multi-objective optimization algorithm based on incrementally Pareto fitness is proposed in this paper. To overcome the directly calculate the Pareto fitness matrix expensively, we adopt to make full use of information of last iteration at each stept to update the Parteto fitness matrix gradually. Experiments proved the highest efficiency of the new method.
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7

Consonni, Guido, Luca La Rocca, and Stefano Peluso. "Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection." Scandinavian Journal of Statistics 44, no. 3 (March 29, 2017): 741–64. http://dx.doi.org/10.1111/sjos.12273.

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8

Diciccio, T. J., and G. A. Young. "Objective Bayes and conditional inference in exponential families." Biometrika 97, no. 2 (April 15, 2010): 497–504. http://dx.doi.org/10.1093/biomet/asq002.

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9

Lupiani, Eduardo, Stewart Massie, Susan Craw, Jose M. Juarez, and Jose Palma. "Case-base maintenance with multi-objective evolutionary algorithms." Journal of Intelligent Information Systems 46, no. 2 (September 21, 2015): 259–84. http://dx.doi.org/10.1007/s10844-015-0378-z.

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10

YASUE, Masaaki, Koichi KOBAYASHI, and Shinichiro TAMORI. "MULTI-OBJECTIVE OPTIMAL DESIGN OF BASE ISOLATED DEVICE IN BASE-ISOLATED STRUCTURE." Journal of Structural and Construction Engineering (Transactions of AIJ) 75, no. 655 (2010): 1645–52. http://dx.doi.org/10.3130/aijs.75.1645.

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11

Jha, N. K. "A Discrete Data Base Multiple Objective Optimization of Milling Operation Through Geometric Programming." Journal of Engineering for Industry 112, no. 4 (November 1, 1990): 368–74. http://dx.doi.org/10.1115/1.2899601.

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The optimum process planning of the milling operation has been attempted through multiple objectives. A multiple objective function based on cost of production and rate of production in milling operations has been developed. The unified objective function thus developed serves as true arbiter balancing the values and objectives of local or individual objective functions. Most of the machine settings are discrete in nature and this has been considered in computerized production planning of milling. The complete approach has been demonstrated through an example.
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12

Rajesh, A. E., J. T. Rubinstein, M. Ferreira, A. P. Patel, R. A. Bly, and G. D. Kohlberg. "Automated objective surgical planning for lateral skull base tumors." International Journal of Computer Assisted Radiology and Surgery 17, no. 3 (January 28, 2022): 427–36. http://dx.doi.org/10.1007/s11548-022-02564-9.

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13

Qu, Yanping, Glen D. Meeden, and Bo Zhang. "An objective stepwise Bayes approach to small area estimation." Journal of Statistical Computation and Simulation 85, no. 7 (January 21, 2014): 1474–94. http://dx.doi.org/10.1080/00949655.2013.879589.

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14

CONSONNI, GUIDO, and LUCA LA ROCCA. "Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models." Scandinavian Journal of Statistics 39, no. 4 (April 12, 2012): 743–56. http://dx.doi.org/10.1111/j.1467-9469.2011.00785.x.

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15

Rukhin, A. L. "Objective Bayes models for compatibility assessment and bias estimation." Journal of Statistical Planning and Inference 182 (March 2017): 74–87. http://dx.doi.org/10.1016/j.jspi.2016.09.006.

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16

Sweeting, T. J. "Coverage probability bias, objective Bayes and the likelihood principle." Biometrika 88, no. 3 (October 1, 2001): 657–75. http://dx.doi.org/10.1093/biomet/88.3.657.

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17

Fujioka, Yoshisato. "Objective Lens System for Variable Thickness of Disk Base." Optical Review 5, no. 2 (March 1998): 109–14. http://dx.doi.org/10.1007/s10043-998-0109-6.

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18

Lai, M. Z., Z. M. Duan, G. Y. Zhang, and B. D. . "Multi-objective Structural Optimization Base on Improved NSGA-II Algorithm." Information Technology Journal 12, no. 23 (November 15, 2013): 7646–50. http://dx.doi.org/10.3923/itj.2013.7646.7650.

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19

KOBAYASHI, Koichi, Shunsuke TSUCHIYA, and Shinichiro TAMORI. "MULTI-OBJECTIVE OPTIMAL DESIGN OF BASE-ISOLATED REINFORCED CONCRETE BUILDING." Journal of Structural and Construction Engineering (Transactions of AIJ) 77, no. 681 (2012): 1645–54. http://dx.doi.org/10.3130/aijs.77.1645.

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20

Lari, Mohammad, and Sina Asaeian. "Multi-objective Antenna Selection in a Full Duplex Base Station." Wireless Personal Communications 110, no. 2 (October 11, 2019): 781–93. http://dx.doi.org/10.1007/s11277-019-06754-8.

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21

Charitidou, E., D. Fouskakis, and I. Ntzoufras. "Objective Bayesian transformation and variable selection using default Bayes factors." Statistics and Computing 28, no. 3 (April 22, 2017): 579–94. http://dx.doi.org/10.1007/s11222-017-9749-3.

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22

Kim, Hyun-Su, and Joo-Won Kang. "Multi-objective fuzzy control of smart base isolated spatial structure." International Journal of Steel Structures 14, no. 3 (September 2014): 547–56. http://dx.doi.org/10.1007/s13296-014-3011-z.

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23

Moreno, Elías, F. Javier Girón, and George Casella. "Consistency of objective Bayes factors as the model dimension grows." Annals of Statistics 38, no. 4 (August 2010): 1937–52. http://dx.doi.org/10.1214/09-aos754.

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24

Santini, Berenice, Ivan Antônio Pinheiro, Louis Hébert, and Janur Mendonça. "Towards an alliance management framework: An investigation on non-declared objectives and the origin of management factors." Base – Revista de Administração e Contabilidade da Unisinos 7, no. 4 (December 31, 2010): 341–51. http://dx.doi.org/10.4013/base.2010.74.07.

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25

Biggs, Felix, and Benjamin Guedj. "Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks." Entropy 23, no. 10 (September 29, 2021): 1280. http://dx.doi.org/10.3390/e23101280.

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We make two related contributions motivated by the challenge of training stochastic neural networks, particularly in a PAC–Bayesian setting: (1) we show how averaging over an ensemble of stochastic neural networks enables a new class of partially-aggregated estimators, proving that these lead to unbiased lower-variance output and gradient estimators; (2) we reformulate a PAC–Bayesian bound for signed-output networks to derive in combination with the above a directly optimisable, differentiable objective and a generalisation guarantee, without using a surrogate loss or loosening the bound. We show empirically that this leads to competitive generalisation guarantees and compares favourably to other methods for training such networks. Finally, we note that the above leads to a simpler PAC–Bayesian training scheme for sign-activation networks than previous work.
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26

Kim, Hyun-Su, and Paul N. Roschke. "Fuzzy Control of Base-Isolation System Using Multi-Objective Genetic Algorithm." Computer-Aided Civil and Infrastructure Engineering 21, no. 6 (August 2006): 436–49. http://dx.doi.org/10.1111/j.1467-8667.2006.00448.x.

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27

Ramchandani, D. "End of Third-Year Objective Structured Clinical Examination: Boon or Bane?" Academic Psychiatry 32, no. 3 (May 1, 2008): 173–76. http://dx.doi.org/10.1176/appi.ap.32.3.173.

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28

Diciccio, T. J., T. A. Kuffner, and G. A. Young. "Objective Bayes, conditional inference and the signed root likelihood ratio statistic." Biometrika 99, no. 3 (July 25, 2012): 675–86. http://dx.doi.org/10.1093/biomet/ass028.

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29

Ryu, Duksan, and Jongmoon Baik. "Effective multi-objective naïve Bayes learning for cross-project defect prediction." Applied Soft Computing 49 (December 2016): 1062–77. http://dx.doi.org/10.1016/j.asoc.2016.04.009.

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30

Fan, Qinqin, and Xuefeng Yan. "Multi-objective modified differential evolution algorithm with archive-base mutation for solving multi-objective $$p$$ p -xylene oxidation process." Journal of Intelligent Manufacturing 29, no. 1 (May 7, 2015): 35–49. http://dx.doi.org/10.1007/s10845-015-1087-8.

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31

Chaudhuri, Abhilasha, and Tirath Prasad Sahu. "Feature weighting for naïve Bayes using multi objective artificial bee colony algorithm." International Journal of Computational Science and Engineering 24, no. 1 (2021): 74. http://dx.doi.org/10.1504/ijcse.2021.113655.

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32

Shin, Minchul, Seonjoo Choi, Jongho Park, Sangyoon Oh, and Chanki Jeong. "Multi-objective Optimization Model for C-UAS Sensor Placement in Air Base." Journal of the Korea Institute of Military Science and Technology 25, no. 2 (April 5, 2022): 125–34. http://dx.doi.org/10.9766/kimst.2022.25.2.125.

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Recently, there are an increased the number of reports on the misuse or malicious use of an UAS. Thus, many researchers are studying on defense schemes for UAS by developing or improving C-UAS sensor technology. However, the wrong placement of sensors may lead to a defense failure since the proper placement of sensors is critical for UAS defense. In this study, a multi-object optimization model for C-UAS sensor placement in an air base is proposed. To address the issue, we define two objective functions: the intersection ratio of interested area and the minimum detection range and try to find the optimized placement of sensors that maximizes the two functions. C-UAS placement model is designed using a NSGA-II algorithm, and through experiments and analyses the possibility of its optimization is verified.
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33

Sahu, Tirath Prasad, and Abhilasha Chaudhuri. "Feature weighting for naïve Bayes using multi objective artificial bee colony algorithm." International Journal of Computational Science and Engineering 24, no. 1 (2021): 74. http://dx.doi.org/10.1504/ijcse.2021.10036006.

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34

Jiang, Jiang, Leilei Chang, Limao Zhang, and Xiaojian Xu. "Retraceable and online multi-objective active optimal control using belief rule base." Knowledge-Based Systems 233 (December 2021): 107553. http://dx.doi.org/10.1016/j.knosys.2021.107553.

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35

Castelletti, Federico, Guido Consonni, Marco L. Della Vedova, and Stefano Peluso. "Learning Markov Equivalence Classes of Directed Acyclic Graphs: An Objective Bayes Approach." Bayesian Analysis 13, no. 4 (December 2018): 1235–60. http://dx.doi.org/10.1214/18-ba1101.

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36

Dai, Haibo, Haiyang Zhang, Baoyun Wang, and Luxi Yang. "The multi-objective deployment optimization of UAV-mounted cache-enabled base stations." Physical Communication 34 (June 2019): 114–20. http://dx.doi.org/10.1016/j.phycom.2019.03.007.

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37

Wasserman, Larry. "Frequentist Bayes is objective (comment on articles by Berger and by Goldstein)." Bayesian Analysis 1, no. 3 (September 2006): 451–56. http://dx.doi.org/10.1214/06-ba116h.

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38

Liu, Jenn Long, Chao Wei Chou, and Chia Mei Chen. "Optimising mobile base station placement using an enhanced Multi-Objective Genetic Algorithm." International Journal of Business Intelligence and Data Mining 5, no. 1 (2010): 19. http://dx.doi.org/10.1504/ijbidm.2010.030297.

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39

Tsipianitis, Alexandros, and Yiannis Tsompanakis. "Multi-Objective Optimization of Base-Isolated Tanks with Supplemental Linear Viscous Dampers." Infrastructures 7, no. 11 (November 17, 2022): 157. http://dx.doi.org/10.3390/infrastructures7110157.

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Base isolation of liquid storage tanks has proven to be an efficient seismic protection measure, leading to a drastic reduction of a superstructure’s distress. However, many such tanks are located near seismic tectonic faults, which generate strong pulse-like ground motions that can impose excessive displacement demands on the isolators. For this reason, viscous dampers are incorporated into the isolation system to avoid overconservative isolators design. To optimize the seismic performance of hybrid isolation systems consisting of single friction pendulum bearings and linear viscous dampers, two novel multi-objective optimization approaches are proposed in the current study. Furthermore, suitable constraint functions and design variables are selected, considering the most critical parameters of the hybrid isolation system. The multi-objective genetic algorithm optimizer is used for the solution of both problems. The results are presented in the typical form of Pareto and certain optimal design solutions are carefully chosen and compared in terms of isolators fragility curves and tank accelerations. The main aim is to optimize the critical design parameters by achieving a reasonable balance among contradicting objectives. The tank industry can substantially benefit from this study, as a more cost-efficient design of hybrid base-isolation can be attained for large-scale tanks.
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40

Viaggi, Davide, Fabio Bartolini, and Meri Raggi. "The Bioeconomy in economic literature: looking back, looking ahead." Bio-based and Applied Economics 10, no. 3 (January 11, 2022): 169–84. http://dx.doi.org/10.36253/bae-10881.

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The objective of this paper is to provide a review of recent economic literature related to the Bioeconomy, in particular aimed at identifying relevant pathways for future research in this field. The paper is organised in four main parts. First, we illustrate the economic role of the Bioeconomy and its key statistics in the EU. Second we review economic topics related to the Bioeconomy production in a mainly private (company, consumer, market) perspective. Then we extend our attention to the review of wider social and environmental aspects with a focus on ecosystem services. Finally, we discuss the interplay of the above topics and cross cutting issues in the attempt to identify the most promising pathways for further research. While the economic literature is growing fast in all the fields of the Bioeconomy, we highlight in particular the need of more economic research focusing on transitions and innovation. However, we also highlight the need to take a system perspective and accounting explicitly for the trade-offs among the many objectives that the Bioeconomy is expected to target and to better account of the costs and benefits affecting different stakeholder groups.
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41

Riley, M., M. Goodman, and V. U. Fritz. "An objective measure of gait using ink footprints." South African Journal of Physiotherapy 55, no. 2 (May 31, 1999): 8–11. http://dx.doi.org/10.4102/sajp.v55i2.560.

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A study was conducted to test the usefulness of seven temporal distance measurements (measurements of time and distance) in the assessment of functional ambulation in stroke patients. These measurements were obtained simply and inexpensively using a paper walkway and ink footprints.The gait of ten hemiparetic patients was initially assessed according to the Massachusetts Functional Ambulation Rating. Each patient was then instructed to walk at his/her most comfortable speed along a ten metre paper walkway while velocity and cadence were measured. Ink pads were then attached to the heel and toe of each shoe and the patients walked again along the walkway.Measurements of step length, stride length, stride length: lower extremity length ratio, foot angle and width of base were recorded and analysed.Velocity and cadence were found to correlate well with the functional ambulation rating. Step length was found to correlate with the functional ambulation rating, but the longer step was not always taken with the affected leg. Stride length and stride length: lower extremity length ratio correlated well with the functional ambulation rating. Foot angle and width of base did not correlate with function but all hemiparetic patients showed a greater foot angle with the affected leg.This method of data collection is inexpensive and easy to use in any physiotherapy department although the data analysis is time-consuming.
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42

OKELLO, Moses Oyaro. "Time Governed Multi-Objective Optimization." Eurasia Proceedings of Science Technology Engineering and Mathematics 16 (December 31, 2021): 167–81. http://dx.doi.org/10.55549/epstem.1068585.

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Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further breaks down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real-world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore, suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.
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43

E., Demirel. "Mathematical model for logistics base selection decision." Scientific Bulletin of Naval Academy XXII, no. 2 (December 15, 2019): 195–203. http://dx.doi.org/10.21279/1454-864x-19-i2-023.

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Logistics bases are defined as a particular region, in which all activities related to transport, logistics and distribution of goods, at both national and international level, is carried out by various operators. In this context, the goal of this study to choose the best available port to be developed as a logistics base in Turkey. A mathematical model called Analytical Hierarchy Process (AHP) model is used as a tool to determine the best location for logistics base in Turkey. After having a preliminary research for alternative locations, existing ports in Ambarlı/İstanbul, Samsun, Mersin and Aliağa/İzmir are designated as alternative bases. During this preliminary review, the existing facilities such as ports, transportation, infrastructure and their potential cargo capabilities to be handled are taken into consideration. In this model, total of five different sets of primary objectives/ choice criteria are defined. These are; current transportation capabilities and its connection with the hinterland, labor force to be employed in the base, current infrastructure capabilities, size of the area covered by the port, cargo potential to be handled in the base and number of the logistics companies in the region. In order to achieve much more accurate result, second and third level sub-objectives are also assigned for each first level objective such as transportation, labor, infrastructure, and area. To achieve a much more comprehensive picture, a sensitivity analysis for input parameters, such as transportation, is also applied. Further on this analysis, inputs (different preference values of transportation objectives) and outputs (port priority values) of this analysis are used to establish a Linear Regression Model for further predictions.
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44

Kono, Kentaro, Masatsugu Hirota, Hidemasa Shinpo, Ema Muraishi, Ai Tokue, Takeshi Toyama, Tohru Hayakawa, and Chikahiro Ohkubo. "Study on the Objective Evaluation of Surface Friction Properties of Denture Base Materials." Annals of Japan Prosthodontic Society 4, no. 3 (2012): 294–301. http://dx.doi.org/10.2186/ajps.4.294.

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45

Chen, Jian, Jiajun Tian, Shuheng Jiang, Yunsheng Zhou, Hai Li, and Jing Xu. "The Allocation of Base Stations with Region Clustering and Single-Objective Nonlinear Optimization." Mathematics 10, no. 13 (June 27, 2022): 2257. http://dx.doi.org/10.3390/math10132257.

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For the problem of 5G network planning, a certain number of locations should be selected to build new base stations in order to solve the weak coverage problems of the existing network. Considering the construction cost and some other factors, it is impossible to cover all the weak coverage areas so it is necessary to consider the business volume and give priority to build new stations in the weak coverage areas with high business volume. Aimed at these problems, the clustering of weak point data was carried out by using k-means clustering algorithm. With the objective function as the minimization of the total construction cost of the new base stations, as well as the constraints as the minimal distance between adjacent base stations and the minimal coverage of the communication traffic, the single-objective nonlinear programming models were established to obtain the layout of macro and micro base stations in order to illustrate the impact of the shape of the station coverage area, the circular and the “shamrock” shaped coverage areas were compared in this paper. For the “shamrock” base station, a secondary clustering was undertaken to judge the main directions of the three sector coverage areas. Then, an improved model taking the coverage overlapping into consideration was proposed to correct the coverage area of different sectors. Finally, the optimal layout was obtained by adjusting the distribution of all base stations globally. The results show that the optimal planning method proposed in this paper has good practicability, which also provides a very good reference for solving similar allocation problems of dynamic resources.
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46

Chen, Guang Hua, De Tian, and Ying Deng. "Main Beam Optimization of Wind Turbine Blade Base on Multi-Objective Genetic Algorithm." Advanced Materials Research 655-657 (January 2013): 496–501. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.496.

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Take s814 airfoil as an example, established the multi-objective optimization model of moment of inertia and the weight for wind turbine blade main beam,Using the genetic algorithm global optimization algorithm, and given the Pareto solution set of optimal with the form of Pareto front. Select four kinds of optimization results scheme to do finite element calculation. The results shows that the magnitude of moment of inertia accordance with the change trend of main beam deflection, the width and thick of beam cap have great affect on moment of inertia and weight, the nearer aerodynamic center of leading edge, the greater moment of inertia, the thick of web almost no influence on moment of inertia.
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47

Girón, F. Javier, Elías Moreno, George Casella, and M. L. Martínez. "Consistency of objective Bayes factors for nonnested linear models and increasing model dimension." Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas 104, no. 1 (March 2010): 57–67. http://dx.doi.org/10.5052/racsam.2010.06.

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48

Cheng, Lei, Lu Zhang, and Wilfried Brutsaert. "Automated Selection of Pure Base Flows from Regular Daily Streamflow Data: Objective Algorithm." Journal of Hydrologic Engineering 21, no. 11 (November 2016): 06016008. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001427.

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49

Wang, Min, and Jun Tang. "Multi-Objective Optimization Algorithm Design and Implementation of Mobile Base Station Site Selection." Applied Mechanics and Materials 29-32 (August 2010): 2496–502. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2496.

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The number of base station location impact the network quality of service. A new method is proposed based on immune genetic algorithm for site selection. The mathematical model of multi-objective optimization problem for base station selection and the realization of the process were given. The use of antibody concentration selection ensures the diversity of the antibody and avoiding the premature convergence, and the use of memory cells to store Pareto optimal solution of each generation. A exclusion algorithm of neighboring memory cells on the updating and deleting to ensure that the Pareto optimal solution set of the distribution. The experiments results show that the algorithm can effectively find a number of possible base station and provide a solution for the practical engineering application.
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50

Kang, Joo-Won, Hyun-Su Kim, and Jun-Ho Lim. "Multi-objective Fuzzy Control of a Spacial Structure using Smart Base Isolation System." Journal of the Korean Association for Spatial Structures 11, no. 2 (June 15, 2011): 89–99. http://dx.doi.org/10.9712/kass.2011.11.2.089.

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