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). 5. Grzegorczyk , M. , Husmeier , D. , Edwards , K. D. , Ghazal , P. , Millar , A. J. : Modelling nonstationary gene regulatory processes with a non-homogeneous Bayesian network and the

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correlated. The interest and originality of our study is that it introduces a new Bayesian network-based approach for analyzing the conditional (in)dependencies between journal citation indices. In this paper, we build some Bayesian networks (yearly

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Abstract  

It is well known from previous research activities that R&D collaboration among economic actors for knowledge production is very important. An accompanying analysis of the impact of R&D collaboration on innovative performance has to be conducted for transferring knowledge to the globalized knowledge-based economy. When we first investigated previous research concerning R&D collaboration, we found some limitations in the analysis methodology. In order to overcome these limitations in previous research, we applied a Bayesian network for analyzing the impact of R&D collaboration in Korean firms on their innovative performance.

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Scientometrics
Authors: Cristhian Ruiz, Ricardo Bonilla, Diego Chavarro, Luis Orozco, Roberto Zarama, and Xavier Polanco

Abstract  

Applications of non-parametric frontier production methods such as Data Envelopment Analysis (DEA) have gained popularity and recognition in scientometrics. DEA seems to be a useful method to assess the efficiency of research units in different fields and disciplines. However, DEA results give only a synthetic measurement that does not expose the multiple relationships between scientific production variables by discipline. Although some papers mention the need for studies by discipline, they do not show how to take those differences into account in the analysis. Some studies tend to homogenize the behaviour of different practice communities. In this paper we propose a framework to make inferences about DEA efficiencies, recognizing the underlying relationships between production variables and efficiency by discipline, using Bayesian Network (BN) analysis. Two different DEA extensions are applied to calculate the efficiency of research groups: one called CCRO and the other Cross Efficiency (CE). A BN model is proposed as a method to analyze the results obtained from DEA. BNs allow us to recognize peculiarities of each discipline in terms of scientific production and the efficiency frontier. Besides, BNs provide the possibility for a manager to propose what-if scenarios based on the relations found.

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Abstract  

This paper present a compound approach for Webometrics based on an extension the self-organizing multimap MultiSOM model. The goal of this new approach is to combine link and domain clustering in order to increase the reliability and the precision of Webometrics studies. The extension proposed for the MultiSOM model is based on a Bayesian network-oriented approach. A first experiment shows that the behaviour of such an extension is coherent with its expected properties for Webometrics. A second experiment is carried out on a representative Web dataset issued from the EISCTES IST project context. In this latter experiment each map represents a particular viewpoint extracted from the Web data description. The obtained maps represented either thematic or link classifications. The experiment shows empirically that the communication between these classifications provides Webometrics with new explaining capabilities.

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sclerosis. Expert Opin. Investig. Drugs, 2012, 21 (9), 1267–1308. 6 Antal, P., Fannes, G., Timmerman, D., et al.: Using literature and data to learn Bayesian

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environmental impacts . J. Amer. Mosq. Control Assoc. 9 ( 2 ): 174 – 181 . Dowe , D.L. 2011 . MML, Hybrid Bayesian network graphical models, statistical consistency

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127 135 Sun, L. — Shenoy, P. P. (2007): Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues. European Journal of Operational Research 180: 738

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, J. R., Wallace, C. S. and Korb, K. B. 1999. Bayesian networks with non-interacting causes. Tech. Rep. 1999/28, Dept. Computer Science, Monash University, Melbourne. Bayesian networks with non

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Orvosi Hetilap
Authors: Nóra Gyöngyösi, Kende Lőrincz, Sarolta Kárpáti, and Norbert Wikonkál

bayesian network meta-analysis. Arch. Dermatol., 2012, 148 , 1403–1410. Devine E. B. Comparison of ustekinumab with other biological agents for the treatment of moderate to severe plaque

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