國立虎尾科技大學 |

Monte Carlo and Quasi-Monte Carlo Methods = MCQMC 2020, Oxford, United Kingdom, August 10–14 /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Monte Carlo and Quasi-Monte Carlo Methods/ edited by Alexander Keller.
其他題名:	MCQMC 2020, Oxford, United Kingdom, August 10–14 /
其他作者:	Keller, Alexander.
面頁冊數:	XVI, 311 p. 69 illus., 53 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optimization. -
電子資源:	https://doi.org/10.1007/978-3-030-98319-2
ISBN:	9783030983192

Monte Carlo and Quasi-Monte Carlo Methods = MCQMC 2020, Oxford, United Kingdom, August 10–14 /
Monte Carlo and Quasi-Monte Carlo MethodsMCQMC 2020, Oxford, United Kingdom, August 10–14 /[electronic resource] :edited by Alexander Keller. - 1st ed. 2022. - XVI, 311 p. 69 illus., 53 illus. in color.online resource. - Springer Proceedings in Mathematics & Statistics,3872194-1017 ;. - Springer Proceedings in Mathematics & Statistics,125.

The MCQMC Conference Series -- The MCQMC Conference Series: P. L’Ecuyer and F. Puchhammer, Density Estimation by Monte Carlo and Quasi-Monte Carlo -- Sou-Cheng T. Choi, Fred J. Hickernell, Rathinavel Jagadeeswaran, Michael J. McCourt, and Aleksei G. Sorokin, Quasi-Monte Carlo Software -- Part II Regular Talks: P. L’Ecuyer, P. Marion, M. Godin, and F. Puchhamme, A Tool for Custom Construction of QMC and RQMC Point Sets -- Art B. Owen, On Dropping the first Sobol’ Point -- C. Lemieux and J. Wiart, On the Distribution of Scrambled Nets over Unanchored Boxes -- S. Heinrich, Lower Bounds for the Number of Random Bits in Monte Carlo Algorithms -- N. Binder, S. Fricke, and A. Keller, Massively Parallel Path Space Filtering -- M. Hird, S. Livingstone, and G. Zanella, A fresh Take on ‘Barker Dynamics’ for MCMC -- P. Blondeel, P. Robbe, S. François, G. Lombaert and S. Vandewalle, On the Selection of Random Field Evaluation Points in the p-MLQMC Method -- S. Si, Chris. J. Oates, Andrew B. Duncan, L. Carin, and François-Xavier Briol, Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization -- Andrei S. Cozma and C. Reisinger, Simulation of Conditional Expectations under fast mean-reverting Stochastic Volatility Models -- M. Huber, Generating from the Strauss Process using stitching -- R. Nasdala and D. Potts, A Note on Transformed Fourier Systems for the Approximation of Non-Periodic Signals -- M. Hofert, A. Prasad, and Mu Zhu, Applications of Multivariate Quasi-Random Sampling with Neural Networks -- A. Keller and Matthijs Van keirsbilck, Artificial Neural Networks generated by Low Discrepancy Sequences.

This volume presents the revised papers of the 14th International Conference in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2020, which took place online during August 10-14, 2020. This book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in statistics, machine learning, finance, and computer graphics, offering information on the latest developments in Monte Carlo and quasi-Monte Carlo methods and their randomized versions.

ISBN: 9783030983192

Standard No.: 10.1007/978-3-030-98319-2doiSubjects--Topical Terms:

669174
Optimization.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Monte Carlo and Quasi-Monte Carlo Methods = MCQMC 2020, Oxford, United Kingdom, August 10–14 /
LDR:03715nam a22004095i 4500 001 1090239
003 DE-He213
005 20220520082856.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030983192 $9 978-3-030-98319-2
024 7 $a 10.1007/978-3-030-98319-2 $2 doi
035 $a 978-3-030-98319-2
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
245 1 0 $a Monte Carlo and Quasi-Monte Carlo Methods $h [electronic resource] : $b MCQMC 2020, Oxford, United Kingdom, August 10–14 / $c edited by Alexander Keller.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XVI, 311 p. 69 illus., 53 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Proceedings in Mathematics & Statistics, $x 2194-1017 ; $v 387
505 0 $a The MCQMC Conference Series -- The MCQMC Conference Series: P. L’Ecuyer and F. Puchhammer, Density Estimation by Monte Carlo and Quasi-Monte Carlo -- Sou-Cheng T. Choi, Fred J. Hickernell, Rathinavel Jagadeeswaran, Michael J. McCourt, and Aleksei G. Sorokin, Quasi-Monte Carlo Software -- Part II Regular Talks: P. L’Ecuyer, P. Marion, M. Godin, and F. Puchhamme, A Tool for Custom Construction of QMC and RQMC Point Sets -- Art B. Owen, On Dropping the first Sobol’ Point -- C. Lemieux and J. Wiart, On the Distribution of Scrambled Nets over Unanchored Boxes -- S. Heinrich, Lower Bounds for the Number of Random Bits in Monte Carlo Algorithms -- N. Binder, S. Fricke, and A. Keller, Massively Parallel Path Space Filtering -- M. Hird, S. Livingstone, and G. Zanella, A fresh Take on ‘Barker Dynamics’ for MCMC -- P. Blondeel, P. Robbe, S. François, G. Lombaert and S. Vandewalle, On the Selection of Random Field Evaluation Points in the p-MLQMC Method -- S. Si, Chris. J. Oates, Andrew B. Duncan, L. Carin, and François-Xavier Briol, Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization -- Andrei S. Cozma and C. Reisinger, Simulation of Conditional Expectations under fast mean-reverting Stochastic Volatility Models -- M. Huber, Generating from the Strauss Process using stitching -- R. Nasdala and D. Potts, A Note on Transformed Fourier Systems for the Approximation of Non-Periodic Signals -- M. Hofert, A. Prasad, and Mu Zhu, Applications of Multivariate Quasi-Random Sampling with Neural Networks -- A. Keller and Matthijs Van keirsbilck, Artificial Neural Networks generated by Low Discrepancy Sequences.
520 $a This volume presents the revised papers of the 14th International Conference in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2020, which took place online during August 10-14, 2020. This book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in statistics, machine learning, finance, and computer graphics, offering information on the latest developments in Monte Carlo and quasi-Monte Carlo methods and their randomized versions.
650 2 4 $a Optimization. $3 669174
650 2 4 $a Functional Analysis. $3 672166
650 2 4 $a Number Theory. $3 672023
650 1 4 $a Statistics. $3 556824
650 0 $a Mathematical optimization. $3 527675
650 0 $a Functional analysis. $3 527706
650 0 $a Number theory. $3 527883
650 0 $a Statistics . $3 1253516
700 1 $a Keller, Alexander. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1392655
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030983185
776 0 8 $i Printed edition: $z 9783030983208
776 0 8 $i Printed edition: $z 9783030983215
830 0 $a Springer Proceedings in Mathematics & Statistics, $x 2194-1009 ; $v 125 $3 1253690
856 4 0 $u https://doi.org/10.1007/978-3-030-98319-2
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)