Robust mixture modelling using the t distribution
Statistics and Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a novel coalescent model for genome-wide evolution of Copy Number Variations
International Journal of Data Mining and Bioinformatics
Machine Learning: A Probabilistic Perspective
Machine Learning: A Probabilistic Perspective
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Hidden semi-Markov models are effective at modelling sequences with succession of homogenous zones by choosing appropriate state duration distributions. To compensate for model mis-specification and provide protection against outliers, we design a robust hidden semi-Markov model with Student's t mixture models as the emission distributions. The proposed approach is used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and glioblastoma multiforme data illustrate the reliability of the technique.