The study of real-life network modeling has become very popular in recent years.An attractive model is the scale-free percolation model on the lattice Zd,d≥1,because it fulfills several stylized facts observed in lar...The study of real-life network modeling has become very popular in recent years.An attractive model is the scale-free percolation model on the lattice Zd,d≥1,because it fulfills several stylized facts observed in large real-life networks.We adopt this model to continuum space which leads to a heterogeneous random-connection model on Rd:Particles are generated by a homogeneous marked Poisson point process on Rd,and the probability of an edge between two particles is determined by their marks and their distance.In this model we study several properties such as the degree distributions,percolation properties and graph distances.展开更多
In estimation and prediction theory,considerable attention is paid to the question of hav-ing unbiased estimators on a global population level.Recent developments in neural network modelling have mainly focused on acc...In estimation and prediction theory,considerable attention is paid to the question of hav-ing unbiased estimators on a global population level.Recent developments in neural network modelling have mainly focused on accuracy on a granular sample level,and the question of unbi-asedness on the population level has almost completely been neglected by that community.We discuss this question within neural network regression models,and we provide methods of receiving unbiased estimators for these models on the global population level.展开更多
文摘The study of real-life network modeling has become very popular in recent years.An attractive model is the scale-free percolation model on the lattice Zd,d≥1,because it fulfills several stylized facts observed in large real-life networks.We adopt this model to continuum space which leads to a heterogeneous random-connection model on Rd:Particles are generated by a homogeneous marked Poisson point process on Rd,and the probability of an edge between two particles is determined by their marks and their distance.In this model we study several properties such as the degree distributions,percolation properties and graph distances.
文摘In estimation and prediction theory,considerable attention is paid to the question of hav-ing unbiased estimators on a global population level.Recent developments in neural network modelling have mainly focused on accuracy on a granular sample level,and the question of unbi-asedness on the population level has almost completely been neglected by that community.We discuss this question within neural network regression models,and we provide methods of receiving unbiased estimators for these models on the global population level.
