Statistical analysis with missing data
Statistical analysis with missing data
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Artificial Intelligence
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A non-parametric multi-scale statistical model for natural images
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The handbook of brain theory and neural networks
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Learning in graphical models
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
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IEEE Transactions on Pattern Analysis and Machine Intelligence
MFDTs: Mean Field Dynamic Trees
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Artificial Intelligence Review
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This paper considers the dynamic tree (DT) model, first introduced in [1]. A dynamic tree specifies a prior over structures of trees, each of which is a forest of one or more tree-structured belief networks (TSBN). In the literature standard tree-structured belief network models have been found to produce "blocky" segmentations when naturally occurring boundaries within an image did not coincide with those of the subtrees in the fixed structure of the network. Dynamic trees have a flexible architecture which allows the structure to vary to create configurations where the subtree and image boundaries align, and experimentation with the model has shown significant improvements.Here we derive an EM-style update based upon mean field inference for learning the parameters of the dynamic tree model and apply it to a database of images of outdoor scenes where all of its parameters are learned. DTs are seen to offer significant improvement in performance over the fixed-architecture TSBN and in a coding comparison the DT achieves 0.294 bits per pixel (bpp) compression compared to 0.378 bpp for lossless JPEG on images of 7 colours.