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2000
ACM
112views Algorithms» more  STOC 2000»
14 years 18 hour ago
A random graph model for massive graphs
We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize ...
William Aiello, Fan R. K. Chung, Linyuan Lu
COMBINATORICS
2006
131views more  COMBINATORICS 2006»
13 years 7 months ago
Encores on Cores
We give a new derivation of the threshold of appearance of the k-core of a random graph. Our method uses a hybrid model obtained from a simple model of random graphs based on rand...
Julie Cain, Nicholas C. Wormald
SAC
2011
ACM
13 years 2 months ago
Slice sampling mixture models
We propose a more efficient version of the slice sampler for Dirichlet process mixture models described by Walker (2007). This sampler allows the fitting of infinite mixture mod...
Maria Kalli, Jim E. Griffin, Stephen G. Walker
ICC
2007
IEEE
14 years 2 months ago
UWB Impulse Radio Receivers Derived from a Gaussian Mixture Interference Model
— One of the main concerns in ultra wide band (UWB) impulse radio (IR) technology is the presence of severe multiple access interference (MAI). Efficient receivers should theref...
Tomaso Erseghe, Valentina Cellini, Gabriele Don&aa...
ICIP
2001
IEEE
14 years 9 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai