This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

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This work deals with the problem of finding the subject that a given set of pages deals with. The algorithms presented have been designed with a view to provide good results when pages that belong to community (or handle some common topic) are presented to them. When a set of pages are presented, the algorithms return a set of words ordered in the decreasing order of relevance. The words among the first few can be used to identify the topic that the set deals with. The algorithms have been tested by providing them with pages returned from a search engine for a given search query and their performance is assessed based on how high the words used in the search query are placed in the list returned by the algorithm.

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