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Gaussian Latent Tree Models and their Statistics. -Thomas Marge (statslab)

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Signal processing strategies and statistics for identifying the presence of evolution in continuous signals is investigated. Consider a feature to be a function on the original signal which contains information about the signal. Under this framework, a model for multivariate Gaussian features observed across related signals is described. The model considers the possibility that some features in the signal are tree amenable while others are not. A model for identifying candidate features using wavelet transforms is also described. Tree amenability is then explored from the perspective of data thresholding. Because of the high type-1 and type-2 error rates of know tests for Gaussian tree amenability, a measure of how tree amenable a feature is has been developed. A methodology is proposed for reconstructing only the tree amenable components of a signal to improve interoperability of the model. Rigorous statistical methods are then defined to test for both tree amenability as well as general structure in the data. To test and better understand these methods, strategies are described to randomly generate tree amenable data.

This talk is part of the Trinity Mathematical Society series.

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