000 03634nam a2200373Ia 4500
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005 20211104064817.0
008 150108n 000 0 eng d
020 _a9781439878859
040 _erda
050 _aQA 276.4
_b.W428 2014
100 _aWeihs, Claus.
_9119668
245 0 _aFoundations of statistical algorithms :
_bwith references to R packages /
_cClaus Weihs, Olaf Mersmann, Uwe Ligges
264 _aBoca Raton :
_bCRC Press,
_c2014
300 _axxv, 473 pages :
_billustrations
_c24 cm
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
338 _avolume
_2rdacarrier
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
650 _aStatistics
_2sears
700 _aLigges, Uwe.
_9119669
700 _aMersmann, Olaf.
_9119670
942 _cREF
999 _c84808
_d84808