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Graph cluster randomization: network exposure to multiple universes
Graph cluster randomization network exposure multiple universes
2013/6/17
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the ove...
Pruning nearest neighbor cluster trees
cluster trees cluster structure subgraphs of a k-NN graph
2011/6/16
Nearest neighbor (k-NN) graphs are widely used
in machine learning and data mining applications,
and our aim is to better understand what
they reveal about the cluster structure of the unknown
und...
Detection of an anomalous cluster in a network
Detecting a cluster of nodes in a network minimax detection Bayesian detection scanstatistic generalized likelihood-ratio test
2010/3/9
We consider the problem of detecting whether or not in a given sensor network, there
is a cluster of sensors which exhibit an “unusual behavior.” Formally, suppose we are given a set of
nodes and at...
Law of the iterated logarithm - cluster points of deterministic and random subsequences
Law of the iterated logarithm cluster points of deterministic random subsequences
2009/9/24
Law of the iterated logarithm - cluster points of deterministic and random subsequences。
The cluster set of {S,(2nLLn)-1/2; EN) in Banach spaces。
Model-Based Inferences from Adaptive Cluster Sampling
Informative sampling MCMC spatial sampling zero-inated count data
2009/9/22
Adaptive cluster sampling is useful for exploring populations of rare
plant and animal species which cluster together because it allows sampling eort
to be concentrated in areas where observed value...
Cluster Allocation Design Networks
Cluster allocation Inuence diagrams Causal inference Identication of policy effects DAGs
2009/9/22
Design Networks expand this frame-
work by including experimental design decision nodes (Madrigal and Smith 2004).
They provide semantics to discuss how a design decision strategy (such as a clus-
...
A note on percolation on Zd：isoperimetric profile via exponential cluster repulsion
percolation parameters exponential cluster repulsion
2009/3/20
We show that for all p>p_c(Z^d) percolation parameters, the probability that the cluster of the origin is finite but has at least t vertices at distance one from the infinite cluster is exponentially ...