, errors of substitution, and correlation between occurrences. Analyses of these data attempt to explain the observed similarity relations and to capture the underlying structure of the objects under study. The theoretical analysis of similarity relations has been dominated by geometric models

of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model

. The definitions follow the general paradigm of known definitions; yet some substantial modifications and simplifications are introduced. The composition operation is the natural `subroutine substitution' operation, formalized by Micali and Rogaway. We consider several standard settings for multi

In this thesis, I constructed a Scheme evaluator with another student, Paul Van Eyk. The evaluator that we designed emulates substitution model evaluation with renaming. It is actually a tail recursive environment model evaluator that has explicit control of its own stack. We provided a front end

context, during software conception or maintenance. We state that this is a substitution problem in any of the two cases. From this statement, we propose a need-aware substitution model that takes into account functional and non-functional properties. 1

, Stanton, and Whitelaw (1997), who find that a 3-factor model explains over 90 percent of Ginnie Mae yields, but that the remaining variation apparently cannot be explained by the changes in the yield curve. 2 In contrast, our multiple-factor model explains only about one-quarter of the variation in credit

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The sequencing of DNA and polypeptides has now become easy and fast. As sequence databases grow, they are not only providing information for biologists, but also introducing new challenges for computer scientists, mathematicians and statisticians. For example, interesting problems in-clude locating genes in long strings of nucleotides or determining protein structure from amino acid

define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human

We derive an expectation maximization algorithm for maximum-likelihood training of substitution rate matrices from multiple sequence alignments. The algorithm can be used to train hidden substitution models, where the structural context of a residue is treated as a hidden variable that can evolve