3 resultados para Bayesian statistical decision theory

em WestminsterResearch - UK


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Attitudes towards legal authorities based on theories of procedural justice have been explored extensively in the criminal and civil justice systems. This has provided considerable empirical evidence concerning the importance of trust and legitimacy in generating cooperation, compliance and decision acceptance. However, not enough attention has been paid to attitudes towards institutions of informal dispute resolution. This paper asks whether the theory of procedural justice applies to the alternative dispute resolution (ADR) context, focusing on ombuds services. What are the predictors of perceptions of procedural justice during the process of dealing with an ombuds, and what factors shape outcome acceptance? These questions are analyzed using a sample of recent ombuds users. The results indicate that outcome favorability is highly correlated with perceived procedural justice, and both predict decision acceptance.

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We analyze democratic equity in council voting games (CVGs). In a CVG, a voting body containing all members delegates decision-making to a (time-varying) subset of its members, as describes, e.g., the relationship between the United Nations General Assembly and the United Nations Security Council (UNSC). We develop a theoretical framework for analyzing democratic equitability in CVGs at both the country and region levels, and for different assumptions regarding preference correlation. We apply the framework to evaluate the equitability of the UNSC, and the claims of those who seek to reform it. We find that the individual permanent members are overrepresented by between 21.3 times (United Kingdom) and 3.8 times (China) from a country-level perspective, while from a region perspective Eastern Europe is the most heavily overrepresented region with more than twice its equitable representation, and Africa the most heavily underrepresented. Our equity measures do not preclude some UNSC members from exercising veto rights, however.

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Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.