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CSDA
2006
142views more  CSDA 2006»
13 years 9 months ago
Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models
Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated ...
Hans J. Skaug, David A. Fournier
TWC
2011
231views more  TWC 2011»
13 years 4 months ago
An Accurate Closed-Form Approximation of the Distributed MIMO Outage Probability
— The mutual information (MI) of multiple-input multiple-output (MIMO) system over Rayleigh fading channel is known to asymptotically follow a normal probability distribution. In...
Fabien Heliot, Reza Hoshyar, Rahim Tafazolli
FOCS
1992
IEEE
14 years 23 days ago
Proof Verification and Hardness of Approximation Problems
The class PCP(f(n), g(n)) consists of all languages L for which there exists a polynomial-time probabilistic oracle machine that uses O(f(n)) random bits, queries O(g(n)) bits of ...
Sanjeev Arora, Carsten Lund, Rajeev Motwani, Madhu...
HEURISTICS
1998
103views more  HEURISTICS 1998»
13 years 9 months ago
Computing Approximate Solutions of the Maximum Covering Problem with GRASP
We consider the maximum covering problem, a combinatorial optimization problem that arises in many facility location problems. In this problem, a potential facility site covers a s...
Mauricio G. C. Resende

Book
361views
15 years 6 months ago
Introduction to Statistical Signal Processing
"A random or stochastic process is a mathematical model for a phenomenon that evolves in time in an unpredictable manner from the viewpoint of the observer. The phenomenon m...
R.M. Gray