Titlebook: Scan Statistics; Joseph Glaz,Joseph Naus,Sylvan Wallenstein Book 2001 Springer Science+Business Media New York 2001 Poisson process.Random

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Phonophobia

Scan Statistics in DNA and Protein Sequence Analysisnd links with T on the other strand, and a C on one strand links with a G on the other strand. Knowing the sequence of bases in one strand automatically gives the sequence in the complementary strand.

Basal-Ganglia

Scanning , Uniform Distributed Points: Bounds-type inequalities that we will use for developing bounds for scan statistic probabilities are usually tigher than the classical Bonferroni inequalities introduced in Bonferroni (1936). Therefore, the classical Bonferroni inequalities will not be discussed here.

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Two-Dimensional Scan Statistics in detecting unusual clustering of these events under the null hypothesis that .. are independent and identically distributed (i.i.d.) nonnegative integer valued random variables from a specified distribution.

Organization

gnition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. In our group, we designed an innovative bioinformatics method c

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they are without any limitations to the scope. It emphasizes the identification of materials - inorganic, organic, organized (biological), common, rare, described or not described in the accessible literatur- as they actually occur in nature and industry, or are met in the investigation of mishaps a

Fresco

Joseph Glaz,Joseph Naus,Sylvan Wallensteinthings as they are without any limitations to the scope. It emphasizes the identification of materials - inorganic, organic, organized (biological), common, rare, described or not described in the accessible literatur- as they actually occur in nature and industry, or are met in the investigation of

Nomogram

Joseph Glaz,Joseph Naus,Sylvan Wallensteinthey are without any limitations to the scope. It emphasizes the identification of materials - inorganic, organic, organized (biological), common, rare, described or not described in the accessible literatur- as they actually occur in nature and industry, or are met in the investigation of mishaps a

boisterous

Joseph Glaz,Joseph Naus,Sylvan Wallensteinut-output data, with gradient learning algorithms. The term ‘neural network Wiener models’ refers to models composed of a linear dynamic model followed by a nonlinear multilayer perceptron model. Both the SISO and MISO Wiener models in their two basic configurations known as a series-parallel and a