Hidden Factor graph models generalise Hidden Markov Models to tree structured data. The distinctive feature of 'treeHFM' is that it learns a transition matrix for first order (sequential) and for second order (splitting) events. It can be applied to all discrete and continuous data that is structured as a binary tree. In the case of continuous observations, 'treeHFM' has Gaussian distributions as emissions.
|Author||Henrik Failmezger, Achim Tresch|
|Bioconductor views||Clustering HiddenMarkovModel|
|Date of publication||2016-09-19 01:45:39|
|Maintainer||Henrik Failmezger <Henrik.Failmezger@googlemail.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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