Random Number Generation and Monte Carlo Methods:
Gespeichert in:
1. Verfasser: | |
---|---|
Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
New York, NY
Springer New York
1998
|
Schriftenreihe: | Statistics and Computing
|
Schlagworte: | |
Online-Zugang: | Volltext |
Beschreibung: | The role of Monte Carlo methods and simulation in all of the sciences has increased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, computational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recognition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statistical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation |
Beschreibung: | 1 Online-Ressource (XIV, 247 p) |
ISBN: | 9781475729603 9781475729627 |
ISSN: | 1431-8784 |
DOI: | 10.1007/978-1-4757-2960-3 |
Internformat
MARC
LEADER | 00000nmm a2200000zc 4500 | ||
---|---|---|---|
001 | BV042421404 | ||
003 | DE-604 | ||
005 | 20180629 | ||
007 | cr|uuu---uuuuu | ||
008 | 150317s1998 |||| o||u| ||||||eng d | ||
020 | |a 9781475729603 |c Online |9 978-1-4757-2960-3 | ||
020 | |a 9781475729627 |c Print |9 978-1-4757-2962-7 | ||
024 | 7 | |a 10.1007/978-1-4757-2960-3 |2 doi | |
035 | |a (OCoLC)863936825 | ||
035 | |a (DE-599)BVBBV042421404 | ||
040 | |a DE-604 |b ger |e aacr | ||
041 | 0 | |a eng | |
049 | |a DE-384 |a DE-703 |a DE-91 |a DE-634 | ||
082 | 0 | |a 519.5 |2 23 | |
084 | |a MAT 000 |2 stub | ||
100 | 1 | |a Gentle, James E. |d 1943- |e Verfasser |0 (DE-588)126727031 |4 aut | |
245 | 1 | 0 | |a Random Number Generation and Monte Carlo Methods |c by James E. Gentle |
264 | 1 | |a New York, NY |b Springer New York |c 1998 | |
300 | |a 1 Online-Ressource (XIV, 247 p) | ||
336 | |b txt |2 rdacontent | ||
337 | |b c |2 rdamedia | ||
338 | |b cr |2 rdacarrier | ||
490 | 0 | |a Statistics and Computing |x 1431-8784 | |
500 | |a The role of Monte Carlo methods and simulation in all of the sciences has increased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, computational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recognition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statistical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation | ||
650 | 4 | |a Statistics | |
650 | 4 | |a Mathematical statistics | |
650 | 4 | |a Statistics and Computing/Statistics Programs | |
650 | 4 | |a Statistik | |
650 | 0 | 7 | |a Zufallsgenerator |0 (DE-588)4191097-7 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Monte-Carlo-Simulation |0 (DE-588)4240945-7 |2 gnd |9 rswk-swf |
689 | 0 | 0 | |a Monte-Carlo-Simulation |0 (DE-588)4240945-7 |D s |
689 | 0 | 1 | |a Zufallsgenerator |0 (DE-588)4191097-7 |D s |
689 | 0 | |8 1\p |5 DE-604 | |
856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4757-2960-3 |x Verlag |3 Volltext |
912 | |a ZDB-2-SMA |a ZDB-2-BAE | ||
940 | 1 | |q ZDB-2-SMA_Archive | |
999 | |a oai:aleph.bib-bvb.de:BVB01-027856821 | ||
883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk |
Datensatz im Suchindex
_version_ | 1804153094409289728 |
---|---|
any_adam_object | |
author | Gentle, James E. 1943- |
author_GND | (DE-588)126727031 |
author_facet | Gentle, James E. 1943- |
author_role | aut |
author_sort | Gentle, James E. 1943- |
author_variant | j e g je jeg |
building | Verbundindex |
bvnumber | BV042421404 |
classification_tum | MAT 000 |
collection | ZDB-2-SMA ZDB-2-BAE |
ctrlnum | (OCoLC)863936825 (DE-599)BVBBV042421404 |
dewey-full | 519.5 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 519 - Probabilities and applied mathematics |
dewey-raw | 519.5 |
dewey-search | 519.5 |
dewey-sort | 3519.5 |
dewey-tens | 510 - Mathematics |
discipline | Mathematik |
doi_str_mv | 10.1007/978-1-4757-2960-3 |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02839nmm a2200481zc 4500</leader><controlfield tag="001">BV042421404</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20180629 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1998 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475729603</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4757-2960-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781475729627</subfield><subfield code="c">Print</subfield><subfield code="9">978-1-4757-2962-7</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4757-2960-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)863936825</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042421404</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gentle, James E.</subfield><subfield code="d">1943-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)126727031</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Random Number Generation and Monte Carlo Methods</subfield><subfield code="c">by James E. Gentle</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer New York</subfield><subfield code="c">1998</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XIV, 247 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Statistics and Computing</subfield><subfield code="x">1431-8784</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">The role of Monte Carlo methods and simulation in all of the sciences has increased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, computational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recognition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statistical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics and Computing/Statistics Programs</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Zufallsgenerator</subfield><subfield code="0">(DE-588)4191097-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Monte-Carlo-Simulation</subfield><subfield code="0">(DE-588)4240945-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Monte-Carlo-Simulation</subfield><subfield code="0">(DE-588)4240945-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Zufallsgenerator</subfield><subfield code="0">(DE-588)4191097-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4757-2960-3</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SMA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SMA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027856821</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
id | DE-604.BV042421404 |
illustrated | Not Illustrated |
indexdate | 2024-07-10T01:21:08Z |
institution | BVB |
isbn | 9781475729603 9781475729627 |
issn | 1431-8784 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027856821 |
oclc_num | 863936825 |
open_access_boolean | |
owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
physical | 1 Online-Ressource (XIV, 247 p) |
psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
publishDate | 1998 |
publishDateSearch | 1998 |
publishDateSort | 1998 |
publisher | Springer New York |
record_format | marc |
series2 | Statistics and Computing |
spelling | Gentle, James E. 1943- Verfasser (DE-588)126727031 aut Random Number Generation and Monte Carlo Methods by James E. Gentle New York, NY Springer New York 1998 1 Online-Ressource (XIV, 247 p) txt rdacontent c rdamedia cr rdacarrier Statistics and Computing 1431-8784 The role of Monte Carlo methods and simulation in all of the sciences has increased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, computational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recognition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statistical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation Statistics Mathematical statistics Statistics and Computing/Statistics Programs Statistik Zufallsgenerator (DE-588)4191097-7 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 s Zufallsgenerator (DE-588)4191097-7 s 1\p DE-604 https://doi.org/10.1007/978-1-4757-2960-3 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Gentle, James E. 1943- Random Number Generation and Monte Carlo Methods Statistics Mathematical statistics Statistics and Computing/Statistics Programs Statistik Zufallsgenerator (DE-588)4191097-7 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd |
subject_GND | (DE-588)4191097-7 (DE-588)4240945-7 |
title | Random Number Generation and Monte Carlo Methods |
title_auth | Random Number Generation and Monte Carlo Methods |
title_exact_search | Random Number Generation and Monte Carlo Methods |
title_full | Random Number Generation and Monte Carlo Methods by James E. Gentle |
title_fullStr | Random Number Generation and Monte Carlo Methods by James E. Gentle |
title_full_unstemmed | Random Number Generation and Monte Carlo Methods by James E. Gentle |
title_short | Random Number Generation and Monte Carlo Methods |
title_sort | random number generation and monte carlo methods |
topic | Statistics Mathematical statistics Statistics and Computing/Statistics Programs Statistik Zufallsgenerator (DE-588)4191097-7 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd |
topic_facet | Statistics Mathematical statistics Statistics and Computing/Statistics Programs Statistik Zufallsgenerator Monte-Carlo-Simulation |
url | https://doi.org/10.1007/978-1-4757-2960-3 |
work_keys_str_mv | AT gentlejamese randomnumbergenerationandmontecarlomethods |