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TSP
2011
197views more  TSP 2011»
12 years 9 months ago
Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods
—This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems fr...
Amadou Gning, Lyudmila Mihaylova, Simon Maskell, S...
7
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ICASSP
2011
IEEE
12 years 6 months ago
Particle algorithms for filtering in high dimensional state spaces: A case study in group object tracking
We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex...
Lyudmila Mihaylova, Avishy Carmi
UAI
1996
13 years 3 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
BMCBI
2007
153views more  BMCBI 2007»
13 years 2 months ago
Estimating genealogies from linked marker data: a Bayesian approach
Background: Answers to several fundamental questions in statistical genetics would ideally require knowledge of the ancestral pedigree and of the gene flow therein. A few examples...
Dario Gasbarra, Matti Pirinen, Mikko J. Sillanp&au...
ICRA
2010
IEEE
151views Robotics» more  ICRA 2010»
13 years 28 days ago
Improving indoor navigation of autonomous robots by an explicit representation of doors
— In the last decades, tremendous progress has been made in the field of autonomous indoor navigation for mobile robots. However, these approaches assume the structural part of ...
Matthias Nieuwenhuisen, Jörg Stückler, S...