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AROBOTS
2011
14 years 5 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox
JMLR
2010
157views more  JMLR 2010»
14 years 4 months ago
Why are DBNs sparse?
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Shaunak Chatterjee, Stuart Russell
TIP
2010
141views more  TIP 2010»
14 years 4 months ago
Efficient Particle Filtering via Sparse Kernel Density Estimation
Particle filters (PFs) are Bayesian filters capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. Recent research in PFs has investigated ways to approp...
Amit Banerjee, Philippe Burlina
TSMC
2010
14 years 4 months ago
Efficient Sensor Selection for Active Information Fusion
In our previous paper [1], we formalized an active information fusion framework based on dynamic Bayesian networks to provide active information fusion. This paper focuses on a ce...
Yongmian Zhang, Qiang Ji
TSMC
2010
14 years 4 months ago
Attack and Flee: Game-Theory-Based Analysis on Interactions Among Nodes in MANETs
In mobile ad hoc networks, nodes have the inherent ability to move. Aside from conducting attacks to maximize their utility and cooperating with regular nodes to deceive them, mali...
Feng Li, Yinying Yang, Jie Wu