000			03689nam a22004215i 4500
001			978-0-387-78165-5
003			DE-He213
005			20251006084418.0
007			cr nn 008mamaa
008			100301s2009 xxu| s |||| 0|eng d
020			_a9780387781655
020			_a99780387781655
024	7		_a10.1007/978-0-387-78165-5 _2doi
100	1		_aLemieux, Christiane. _eauthor.
245	1	0	_aMonte Carlo and Quasi-Monte Carlo Sampling _h[electronic resource] / _cby Christiane Lemieux.
250			_a1.
264		1	_aNew York, NY : _bSpringer New York, _c2009.
300			_aXIV, 373p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aThe Monte Carlo Method -- Sampling from Known Distributions -- Pseudorandom Number Generators -- Variance Reduction Techniques -- Quasi-Monte Carlo Constructions -- Using Quasi-Monte Carlo in Practice -- Financial Applications -- Beyond Numerical Integration.
520			_aQuasi-Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the last two decades. Their successful implementation on practical problems, especially in finance, has motivated the development of several new research areas within this field to which practitioners and researchers from various disciplines currently contribute. This book presents essential tools for using quasi-Monte Carlo sampling in practice. The first part of the book focuses on issues related to Monte Carlo methods-uniform and non-uniform random number generation, variance reduction techniques-but the material is presented to prepare the readers for the next step, which is to replace the random sampling inherent to Monte Carlo by quasi-random sampling. The second part of the book deals with this next step. Several aspects of quasi-Monte Carlo methods are covered, including constructions, randomizations, the use of ANOVA decompositions, and the concept of effective dimension. The third part of the book is devoted to applications in finance and more advanced statistical tools like Markov chain Monte Carlo and sequential Monte Carlo, with a discussion of their quasi-Monte Carlo counterpart. The prerequisites for reading this book are a basic knowledge of statistics and enough mathematical maturity to follow through the various techniques used throughout the book. This text is aimed at graduate students in statistics, management science, operations research, engineering, and applied mathematics. It should also be useful to practitioners who want to learn more about Monte Carlo and quasi-Monte Carlo methods and researchers interested in an up-to-date guide to these methods. Christiane Lemieux is an Associate Professor and the Associate Chair for Actuarial Science in the Department of Statistics and Actuarial Science at the University of Waterloo in Canada. She is an Associate of the Society of Actuaries and was the winner of a "Young Researcher Award in Information-Based Complexity" in 2004.
650		0	_aSTATISTICS.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387781648
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-78165-5 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59047 _d59047