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Conflict analysis using Bayesian neural networks and generalized linear models / N. Iswaran in Journal of the operational research society (JORS), Vol. 61 N° 2 (Fevrier 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 2 (Fevrier 2010) . - pp. 332–341 Titre : Conflict analysis using Bayesian neural networks and generalized linear models Type de document : texte imprimé Auteurs : N. Iswaran, Auteur ; D. F. Percy, Auteur Année de publication : 2011 Article en page(s) : pp. 332–341 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Bayesian  inference  Conflict  analysis  Generalized  linear  models  Neural  networks Index. décimale : 001.424 Résumé : The study of conflict analysis has recently become more important due to current world events. Despite numerous quantitative analyses on the study of international conflict, the statistical results are often inconsistent with each other. The causes of conflict, however, are often stable and replicable when the prior probability of conflict is large. As there has been much conjecture about neural networks being able to cope with the complexity of such interconnected and interdependent data, we formulate a statistical version of a neural network model and compare the results to those of conventional statistical models. We then show how to apply Bayesian methods to the preferred model, with the aim of finding the posterior probabilities of conflict outbreak and hence being able to plan for conflict prevention. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n2/abs/jors2008183a.html [article] Conflict analysis using Bayesian neural networks and generalized linear models [texte imprimé] / N. Iswaran, Auteur ; D. F. Percy, Auteur . - 2011 . - pp. 332–341. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 2 (Fevrier 2010) . - pp. 332–341 Mots-clés : Bayesian  inference  Conflict  analysis  Generalized  linear  models  Neural  networks Index. décimale : 001.424 Résumé : The study of conflict analysis has recently become more important due to current world events. Despite numerous quantitative analyses on the study of international conflict, the statistical results are often inconsistent with each other. The causes of conflict, however, are often stable and replicable when the prior probability of conflict is large. As there has been much conjecture about neural networks being able to cope with the complexity of such interconnected and interdependent data, we formulate a statistical version of a neural network model and compare the results to those of conventional statistical models. We then show how to apply Bayesian methods to the preferred model, with the aim of finding the posterior probabilities of conflict outbreak and hence being able to plan for conflict prevention. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n2/abs/jors2008183a.html