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ICASSP
2009
IEEE
14 years 2 months ago
Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks
We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial ...
Ondrej Hlinka, Franz Hlawatsch
ALMOB
2006
80views more  ALMOB 2006»
13 years 7 months ago
Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics
The technique of Finite Markov Chain Imbedding (FMCI) is a classical approach to complex combinatorial problems related to sequences. In order to get efficient algorithms, it is k...
Grégory Nuel
EOR
2007
69views more  EOR 2007»
13 years 7 months ago
Incorporating inventory and routing costs in strategic location models
We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and ea...
Zuo-Jun Max Shen, Lian Qi
GECCO
2007
Springer
210views Optimization» more  GECCO 2007»
14 years 1 months ago
Markov chain models of bare-bones particle swarm optimizers
We apply a novel theoretical approach to better understand the behaviour of different types of bare-bones PSOs. It avoids many common but unrealistic assumptions often used in an...
Riccardo Poli, William B. Langdon
CISS
2008
IEEE
14 years 2 months ago
Near optimal lossy source coding and compression-based denoising via Markov chain Monte Carlo
— We propose an implementable new universal lossy source coding algorithm. The new algorithm utilizes two wellknown tools from statistical physics and computer science: Gibbs sam...
Shirin Jalali, Tsachy Weissman