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139
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CSDA
2006
142views more  CSDA 2006»
15 years 4 months ago
Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models
Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated ...
Hans J. Skaug, David A. Fournier
158
Voted
ICASSP
2011
IEEE
14 years 7 months ago
Gaussian mixture modeling for source localization
Exploiting prior knowledge, we use Bayesian estimation to localize a source heard by a fixed sensor network. The method has two main aspects: Firstly, the probability density fun...
John T. Flåm, Joakim Jalden, Saikat Chatterj...
CVPR
2003
IEEE
16 years 6 months ago
Video Segmentation Based on Graphical Models
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field...
Kia-Fock Loe, Tele Tan, Yang Wang 0002
112
Voted
ICIP
1994
IEEE
16 years 5 months ago
Tomographic Reconstruction Based on Flexible Geometric Models
When dealing with ill-posed inverse problems in data analysis, the Bayesian approach allows one to use prior information to guide the result toward reasonable solutions. In this w...
K. M. Hanson, G. S. Cunningham, G. R. Jennings Jr....
154
Voted
ICPR
2008
IEEE
16 years 5 months ago
Kernel bandwidth estimation in methods based on probability density function modelling
In kernel density estimation methods, an approximation of the data probability density function is achieved by locating a kernel function at each data location. The smoothness of ...
Adrian G. Bors, Nikolaos Nasios