Search results for " regularisation."

showing 3 items of 3 documents

Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

2022

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…

FOS: Computer and information sciencesComputer Science - Machine LearningArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceGeneral Earth and Planetary SciencesAI in banking; personalized services; prosperity management; explainable AI; reinforcement learning; policy regularisationVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550General Environmental ScienceMachine Learning (cs.LG)AI; Volume 3; Issue 2; Pages: 526-537
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Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range

2014

A Bayesian solution is suggested for the modelling of spatial point patterns with inhomogeneous hard-core radius using Gaussian processes in the regularization. The key observation is that a straightforward use of the finite Gibbs hard-core process likelihood together with a log-Gaussian random field prior does not work without penalisation towards high local packing density. Instead, a nearest neighbour Gibbs process likelihood is used. This approach to hard-core inhomogeneity is an alternative to the transformation inhomogeneous hard-core modelling. The computations are based on recent Markovian approximation results for Gaussian fields. As an application, data on the nest locations of Sa…

Statistics and ProbabilityMathematical optimizationGaussianBayesian probabilityBayesian analysisMarkov processRegularization (mathematics)symbols.namesakeGaussian process regularisationPERFECT SIMULATIONRange (statistics)Statistical physicsGaussian processMathematicsta113ta112Random fieldApplied MathematicsInhomogeneousSand Martin's nestsTRANSFORMATIONHard-core point processComputational MathematicsTransformation (function)Computational Theory and MathematicssymbolsINFERENCECOMPUTATIONAL STATISTICS AND DATA ANALYSIS
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Yearly quotas and country-reserved shares in Italian immigration policy

2008

Regular immigration to Italy is based on a quota system setting annual ceilings to legal entries. Reserved shares are granted to single countries or categories of countries. Reserved shares have been increased; they are used as an incentive to obtain the cooperation of countries of origin in stemming irregular migration flows. The total quota of regular immigration has gradually increased too. Still, it does not fully respond to the growing demand of foreign workers on the labour market, and quotas seem to be used as crypto-regularisations rather than as an instrument for regulating legal entries.

media_common.quotation_subjectGeography Planning and DevelopmentImmigrationIrregular migrationInternational economicsIncentiveMarket economyImmigration policyEconomicsImmigration lawItaly; legal immigration; entry quotas; reserved shares; regularisationDemographymedia_commonItaly legal immigration entry quotas reserved shares regularisation.
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