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008			210208s2021 gw | s |||| 0|eng d
020			_a9783030562595
024	7		_a10.1007/978-3-030-56259-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aTK145 _b2021 EB
100	1		_aShankar, P. M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678459
245	1	0	_aProbability, Random Variables, and Data Analytics with Engineering Applications _cby P. Mohana Shankar
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XII, 473 páginas) _b206 ilustraciones, 202 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aChapter 1. Introduction -- Chapter 2. Sets, Venn diagrams, Probability and Bayes' Rule -- Chapter 3. Concept of a random variable -- Chapter 4. Multiple random variables and their Characteristics -- Chapter 5. Applications to Data Analytics and Modeling.
520	3		_aThis book bridges the gap between theory and applications that currently exist in undergraduate engineering probability textbooks. It offers examples and exercises using data (sets) in addition to traditional analytical and conceptual ones. Conceptual topics such as one and two random variables, transformations, etc. are presented with a focus on applications. Data analytics related portions of the book offer detailed coverage of receiver operating characteristics curves, parametric and nonparametric hypothesis testing, bootstrapping, performance analysis of machine vision and clinical diagnostic systems, and so on. With Excel spreadsheets of data provided, the book offers a balanced mix of traditional topics and data analytics expanding the scope, diversity, and applications of engineering probability. This makes the contents of the book relevant to current and future applications students are likely to encounter in their endeavors after completion of their studies. A full suite of classroom material is included. A solutions manual is available for instructors. Bridges the gap between conceptual topics and data analytics through appropriate examples and exercises; Features 100's of exercises comprising of traditional analytical ones and others based on data sets relevant to machine vision, machine learning and medical diagnostics; Intersperses analytical approaches with computational ones, providing two-level verifications of a majority of examples and exercises.
988			_aSpringer_Engineering_2021
650		7	_2embne _9138109 _aElectrotecnia
650		7	_2embne _aMatemáticas aplicadas _9145503
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-56259-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eb _zSI