caption: Summary <PERSON> models allow us to model dependencies in sequential data <PERSON> models are described by --states, each state corresponding to a letter in our observed alphabet --transition probabilities between states Parameter estimation can be done by counting the number of transitions between consecutive states (first order) --Laplace correction is often applied Simple example application: classification of genomic region as as CpG islands or not Major application in genomics: gene finding and other genomic segmentation tasks Extensions of <PERSON> models --Higher-order --Inhomogeneous
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