000			03238nam a2200337 i 4500
003			CSPC
005			20230809150641.0
008			230807s2022 nju b 001 0 eng
010			_a 2021031022
020			_a9781118123331 _q(hardback)
040			_aDLC _beng _erda _cDLC _dCSPC
050	0	0	_aQA273 _b.B254727 2022
082	0	0	_a519.2 _bB182i _223
100	1		_aBalakrishnan, N., _d1956- _eauthor.
245	1	0	_aIntroduction to probability : _bmultivariate models and applications / _cN. Balakrishnan, Markos V. Koutras, and Konstantinos G. Politis.
250			_aSecond edition.
264		1	_aHoboken, New Jersey : _bJohn Wiley and Sons, Inc., _c2022.
300			_axv, 528 pages : _billustrations ; _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aTwo-dimensional discrete random variables and distributions -- Two-dimensional continuous random variables and distributions -- Independence and multivariate distributions -- Transformations of variables -- Covariance and correlation -- Important multivariate distributions -- Generating functions -- Limit theorems.
520			_a"Probability theory is the branch of mathematics concerned with analysis of random phenomena. The central objects of probability theory are random variables, stochastic processes, and events: mathematical abstractions of non-deterministic events or measured quantities that may either be single occurrences or evolve over time in an apparently random fashion. Although an individual coin toss or the roll of a die is a random event, if repeated many times the sequence of random events exhibits certain statistical patterns, which can be studied and predicted. Two representative mathematical results describing such patterns are the law of large numbers and the central limit theorem. As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of large sets of data. Methods of probability theory also apply to descriptions of complex systems given only partial knowledge of their state, as in statistical mechanics. The multivariate normal distribution or multivariate Gaussian distribition, is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. A random vector is said to be multivariate normally distributed if every linear combination of its components has a univariate normal distribution. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables, each of which clusters around a mean value"-- _cProvided by publisher.
650		0	_aProbabilities _vTextbooks.
700	1		_aKoutras, Markos V., _eauthor.
700	1		_aKonstantinos, Politis, _d1966- _eauthor.
776	0	8	_iOnline version: _aBalakrishnan, N., 1956- _tIntroduction to probability _bSecond edition. _dHoboken, NJ : Wiley, 2022 _z9781118548622 _w(DLC) 2021031023
942			_2ddc _cBK _h519.2 _iB182i _kCIR _m2022 _n0 _e23
999			_c25846 _d25846