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ICASSP
2009
IEEE
15 years 6 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»
14 years 11 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»
14 years 11 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»
15 years 5 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
15 years 6 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