000 02237nam a2200349 i 4500
001 CR9781108635349
003 UkCbUP
005 20240301142639.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 180606s2019||||enk o ||1 0|eng|d
020 _a9781108635349 (ebook)
020 _z9781108480536 (hardback)
020 _z9781108727709 (paperback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aTA340
_b.P84 2019
082 0 0 _a519.2
_223
100 1 _aPrugel-Bennett, Adam,
_d1963-
_eauthor.
245 1 4 _aThe probability companion for engineering and computer science /
_cAdam Prugel-Bennett.
264 1 _aCambridge :
_bCambridge University Press,
_c2019.
300 _a1 online resource (xv, 457 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 15 Jan 2020).
520 _aThis 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 0 _aEngineering
_xStatistical methods.
650 0 _aComputer science
_xStatistical methods.
650 0 _aProbabilities.
776 0 8 _iPrint version:
_z9781108480536
856 4 0 _uhttps://doi.org/10.1017/9781108635349
999 _c9940
_d9940