000			03106nam a2200397 i 4500
001			CR9781139103947
003			UkCbUP
005			20240802172846.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			110627s2014||||enk o ||1 0|eng|d
020			_a9781139103947 (ebook)
020			_z9781107020405 (hardback)
020			_z9781107652521 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQC20.7.M43 _bL43 2014
082	0	0	_a515/.42 _223
100	1		_aLeadbetter, M.R, _eauthor.
245	1	2	_aA basic course in measure and probability : _btheory for applications / _cRoss Leadbetter, University of North Carolina, Chapel Hill, Stamatis Cambanis, University of North Carolina, Chapel Hill, Vladas Pipiras, University of North Carolina, Chapel Hill.
246	3		_aA Basic Course in Measure & Probability
264		1	_aCambridge : _bCambridge University Press, _c2014.
300			_a1 online resource (xiv, 360 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 05 Oct 2015).
505	8		_aMachine generated contents note: Preface; Acknowledgements; 1. Point sets and certain classes of sets; 2. Measures: general properties and extension; 3. Measurable functions and transformations; 4. The integral; 5. Absolute continuity and related topics; 6. Convergence of measurable functions, Lp-spaces; 7. Product spaces; 8. Integrating complex functions, Fourier theory and related topics; 9. Foundations of probability; 10. Independence; 11. Convergence and related topics; 12. Characteristic functions and central limit theorems; 13. Conditioning; 14. Martingales; 15. Basic structure of stochastic processes; References; Index.
520			_aOriginating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.
650		0	_aMeasure theory.
650		0	_aProbabilities.
700	1		_aCambanis, Stamatis, _d1943-1995, _eauthor.
700	1		_aPipiras, Vladas, _eauthor.
776	0	8	_iPrint version: _z9781107020405
856	4	0	_uhttps://doi.org/10.1017/CBO9781139103947
942			_2ddc _cEB
999			_c9304 _d9304