Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and Statistical Foundations

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Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, methodology): “ 2002; Hofmann et al., 2008). Examples include kernels for images in computer vision (Lazebnik et al., 2006), graph structured data in bioinformatics (Scholkopf et al.,¨ 2004), and genomic sequences (Schaid, 2010a,b). Therefore we can apply KMCF to 3 such structured data by making use of the kernels”
Model selection and psychological theory: A discussion of the differences between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) (2012, background): “For an extensive but accessible discussion of loss functions see Schaid (2010).”