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Method of conditional probabilities

Known as: Pessimistic estimator 
In mathematics and computer science, the probabilistic method is used to prove the existence of mathematical objects with desired combinatorial… 
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Related topics

Related topics

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Approximation algorithmComputer scienceConvex functionDeterministic algorithm

Papers overview

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2013
2013
Gossip-Based Averaging Estimation and Optimization
Motivated by applications to sensor, peer to peer, and ad hoc networks, we study the problem of gossip-based averaging estimation… 
2012
2012
Sliding-sweep soft-decoding of nonbinary linear block codes
A backward (BW) algorithm to generate the optimum syndrome trellis for soft-decision decoding of non-binary linear block codes is… 
2012
2012
Scheduling with pairwise XORing of packets under statistical overhearing information and feedback
We study the problem of scheduling packets from several flows traversing a given node which can mix packets belonging to… 
2011
2011
Polynomial computation of exact correlated equilibrium in compact games
In a landmark paper, Papadimitriou and Roughgarden described a polynomial-time algorithm ("Ellipsoid Against Hope") for computing… 
2009
2009
On the origin of divergences in the coincidence probabilities in cavity photodetection experiments
The theory of photon correlation is an established part of quantum electronics. However, recently reported divergences in the… 
2006
2006
An Embedded Bayesian Network Hidden Markov Model for Digital Forensics
In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov… 
2001
2001
This is what the leading indicators lead 1
We propose an optimal ...lter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the… 
1998
1998
The performance of a link with multipriority traffic
This letter discusses the performance of a link carrying calls of preemptive priorities. General /spl kappa/-dimensional steady… 
1997
1997
Equivalent error bars for neural network classifiers trained by Bayesian inference
The topic of this paper is the problem of outlier detection for neural networks trained by Bayesian inference. I will show that… 
1997
1997
Support and Plausibility Degrees in Generalized Functional Models
By discussing several examples, the theory of generalized functional models is shown to be very natural for modeling some… 
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