نتایج جستجو برای: nearest neighbor model

تعداد نتایج: 2133366  

Journal: :The Annals of Statistics 1999

2012
Mikko Stenlund

Central limit theorems for random walks in quenched random environments have attracted plenty of attention in the past years. More recently still, finer local limit theorems — yielding a Gaussian density multiplied by a highly oscillatory modulating factor — for such models have been obtained. In the one-dimensional nearest-neighbor case with i.i.d. transition probabilities, local limits of uni...

Journal: :The Annals of Probability 1998

Journal: :IEEE Transactions on Information Theory 2022

It is well known that for Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between codeword and received channel output vector, maximum likelihood solution hence capacity-achieving. Nearest remains convenient yet mismatched general key message of this paper performance can be improved by generalizing its metric to incorporate state dependent proces...

V. Fakoor

Kernel density estimators are the basic tools for density estimation in non-parametric statistics.  The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. In this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

Journal: :Discrete & Computational Geometry 1997

Journal: :IEEE Transactions on Information Theory 1967

2013
Christa Nilsen John Paige Olivia Warner Benjamin Mayhew Ryan Sutley Matthew Lam Andrew J. Bernoff Chad M. Topaz

From bird flocks to fish schools and ungulate herds to insect swarms, social biological aggregations are found across the natural world. An ongoing challenge in the mathematical modeling of aggregations is to strengthen the connection between models and biological data by quantifying the rules that individuals follow. We model aggregation of the pea aphid, Acyrthosiphon pisum. Specifically, we ...

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