000 | 03634nam a2200373Ia 4500 | ||
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001 | 271504 | ||
003 | 0000000000 | ||
005 | 20211104064817.0 | ||
008 | 150108n 000 0 eng d | ||
020 | _a9781439878859 | ||
040 | _erda | ||
050 |
_aQA 276.4 _b.W428 2014 |
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100 |
_aWeihs, Claus. _9119668 |
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245 | 0 |
_aFoundations of statistical algorithms : _bwith references to R packages / _cClaus Weihs, Olaf Mersmann, Uwe Ligges |
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264 |
_aBoca Raton : _bCRC Press, _c2014 |
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300 |
_axxv, 473 pages : _billustrations _c24 cm |
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336 |
_atext _2rdacontent |
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337 |
_aunmediated _2rdamedia |
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338 |
_avolume _2rdacarrier |
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500 | _aA Chapman & Hall Book. | ||
504 | _aIncludes bibliography and index. | ||
520 | _a 2) Emphasizes recurring themes in all statistical algorithms: Computation, Assessment and Verification, Iteration, Intuition, Randomness, Repetition and Parallelization, and Scalability | ||
520 | _a 3) Discusses two topics not included in other books: systematic verification and scalability | ||
520 | _a 4) Contains examples, exercises, and selected solutions in each chapter | ||
520 | _a 5) Offers access to a supplementary website. | ||
520 | _aA new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs. Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful. It Features:1) Covers historical development as this is clarifies the evolution of more powerful statistical algorithms | ||
520 | _aReviewing the historical development of basic algorithms to illuminate the evolution of today's more powerful statistical algorithms, this comprehensive textbook emphasizes recurring themes in all statistical algorithms including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, it touches on topics not usually covered in similar books, namely, systematic verification and the scaling of many established techniques to very large databases. Broadly accessible, it offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. | ||
650 |
_aAlgorithms. _920852 |
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650 |
_aStatistics _2sears |
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700 |
_aLigges, Uwe. _9119669 |
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700 |
_aMersmann, Olaf. _9119670 |
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942 | _cREF | ||
999 |
_c84808 _d84808 |