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From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science

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Detalles del libro:

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Año:2016
Editor:University of California
Páginas:409 páginas
Idioma:inglés
Desde:21/10/2016
Tamaño:2.07 MB
Licencia:CC-BY-ND

Contenido:

The materials here form a textbook for a course in mathematical probability and statistics for computer science students. (It would work fine for general students too.)

"Why is this text different from all other texts?"

  • Computer science examples are used throughout, in areas such as: computer networks; data and text mining; computer security; remote sensing; computer performance evaluation; software engineering; data management; etc.
  • The R statistical/data manipulation language is used throughout. Since this is a computer science audience, a greater sophistication in programming can be assumed.
  • Throughout the units, mathematical theory and applications are interwoven, with a strong emphasis on modeling: What do probabilistic models really mean, in real-life terms? How does one choose a model? How do we assess the practical usefulness of models?
  • For instance, the chapter on continuous random variables begins by explaining that such distributions do not actually exist in the real world, due to the discreteness of our measuring instruments. The continuous model is therefore just that--a model, and indeed a very useful model.
  • There is considerable discussion of the intuition involving probabilistic concepts, and the concepts themselves are defined through intuition. However, all models and so on are described precisely in terms of random variables and distributions.

For topical coverage, see the book's detailed table of contents.

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