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One solution for this problem is non-parametric generalized belief propagation based on junction tree.
Another solution is generalized belief propagation based on junction tree (GBP-JT) method [18], which is a standard method for the exact inference in graphical models.
In [19], non-parametric generalized belief propagation based on junction tree (NGBP-JT) has been applied for the localization in a small-scale network, where it has been showed that it can outperform NBP in terms of accuracy, but with an additional cost.
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Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm.
Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT).
Therefore, in this article, we propose non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT).
A tree volume estimate is usually based on three parameters: tree species, tree diameter and tree height.
We propose an efficient algorithm based on a tree selection approach and pre-computed junction-dependent configuration spaces to accelerate the optimization process in a high-dimensional, mixed discrete continuous search space.
The phylogenetic trees shown are based on the tree provided by Ensembl on http://tinyurl.com/ensembltree.
18 Clusters were clinically characterised based on the tree.
The method, which is based on approximating the junction-tree, improves performance with a reduced number of particles with respect to other NBP algorithms in the literature.
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