The probability companion for engineering and computer science:
This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2019
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UBA01 Volltext |
| Zusammenfassung: | This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 15 Jan 2020) |
| Beschreibung: | 1 Online-Ressource (xv, 457 Seiten) |
| ISBN: | 9781108635349 |
| DOI: | 10.1017/9781108635349 |
Internformat
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| 520 | |a This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students | ||
| 650 | 4 | |a Engineering / Statistical methods | |
| 650 | 4 | |a Computer science / Statistical methods | |
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Datensatz im Suchindex
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| author | Prugel-Bennett, Adam 1963- |
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| author_facet | Prugel-Bennett, Adam 1963- |
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| author_sort | Prugel-Bennett, Adam 1963- |
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| building | Verbundindex |
| bvnumber | BV046437900 |
| classification_rvk | SK 850 SK 950 ST 600 |
| collection | ZDB-20-CBO |
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| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| doi_str_mv | 10.1017/9781108635349 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:44:36Z |
| institution | BVB |
| isbn | 9781108635349 |
| language | English |
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| physical | 1 Online-Ressource (xv, 457 Seiten) |
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| publisher | Cambridge University Press |
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| spelling | Prugel-Bennett, Adam 1963- (DE-588)1203910398 aut The probability companion for engineering and computer science Adam Prugel-Bennett Cambridge Cambridge University Press 2019 1 Online-Ressource (xv, 457 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 15 Jan 2020) This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students Engineering / Statistical methods Computer science / Statistical methods Probabilities Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd rswk-swf Statistik (DE-588)4056995-0 s Wahrscheinlichkeitsverteilung (DE-588)4121894-2 s Wahrscheinlichkeitstheorie (DE-588)4079013-7 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 978-1-108-48053-6 https://doi.org/10.1017/9781108635349 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Prugel-Bennett, Adam 1963- The probability companion for engineering and computer science Engineering / Statistical methods Computer science / Statistical methods Probabilities Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd Statistik (DE-588)4056995-0 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd |
| subject_GND | (DE-588)4079013-7 (DE-588)4056995-0 (DE-588)4121894-2 |
| title | The probability companion for engineering and computer science |
| title_auth | The probability companion for engineering and computer science |
| title_exact_search | The probability companion for engineering and computer science |
| title_full | The probability companion for engineering and computer science Adam Prugel-Bennett |
| title_fullStr | The probability companion for engineering and computer science Adam Prugel-Bennett |
| title_full_unstemmed | The probability companion for engineering and computer science Adam Prugel-Bennett |
| title_short | The probability companion for engineering and computer science |
| title_sort | the probability companion for engineering and computer science |
| topic | Engineering / Statistical methods Computer science / Statistical methods Probabilities Wahrscheinlichkeitstheorie (DE-588)4079013-7 gnd Statistik (DE-588)4056995-0 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd |
| topic_facet | Engineering / Statistical methods Computer science / Statistical methods Probabilities Wahrscheinlichkeitstheorie Statistik Wahrscheinlichkeitsverteilung |
| url | https://doi.org/10.1017/9781108635349 |
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