000			01960nam a22003015i 4500
001			91322
005			20231026104027.0
010			_a978-3-031-06784-6 _dcompra
090			_a91322
100			_a20231023d2022 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aStatistical inference and machine learning for big data _bDocumento eletrónico _fby Mayer Alvo
210			_aCham _cSpringer International Publishing _cSpringer _d2022
215			_aXXIV, 431 p. _cil.
225	2		_aSpringer Series in the Data Sciences
303			_aThis book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems. The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented. This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.
606			_aMathematical statistics
606			_aStatistics
606			_aMachine learning
606			_aArtificial intelligence _xData processing
680			_aQA276-280
700	1		_aAlvo _bMayer
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-031-06784-6
942			_2lcc _cF _n0