000			02209nam a22003498i 4500
001			CR9780511810633
003			UkCbUP
005			20200124160218.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s1998||||enk o ||1 0|eng|d
020			_a9780511810633 (ebook)
020			_z9780521481816 (hardback)
020			_z9780521633963 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA274.7 _b.N67 1998
082	0	0	_a519.2/33 _220
100	1		_aNorris, J. R. _q(James R.), _eauthor.
245	1	0	_aMarkov chains / _cJ.R. Norris.
264		1	_aCambridge : _bCambridge University Press, _c1998.
300			_a1 online resource (xvi, 237 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v2
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aMarkov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications.
650		0	_aMarkov processes.
776	0	8	_iPrint version: _z9780521481816
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v2.
856	4	0	_uhttps://doi.org/10.1017/CBO9780511810633
999			_c516476 _d516474