000			01965nam a22003137a 4500
001			sulb-eb0015415
003			BD-SySUS
005			20160405134434.0
008			110516s2013||||enk o ||1 0|eng|d
020			_a9781139087698 (ebook)
020			_z9781107018396 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA274.75 _b.H37 2013
100	1		_aHarrison, J. Michael, _eauthor.
245	1	0	_aBrownian Models of Performance and Control / _cJ. Michael Harrison.
246	3		_aBrownian Models of Performance & Control
264		1	_aCambridge : _bCambridge University Press, _c2013.
300			_a1 online resource (205 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 04 Apr 2016).
520			_aDirect and to the point, this book from one of the field's leaders covers Brownian motion and stochastic calculus at the graduate level, and illustrates the use of that theory in various application domains, emphasizing business and economics. The mathematical development is narrowly focused and briskly paced, with many concrete calculations and a minimum of abstract notation. The applications discussed include: the role of reflected Brownian motion as a storage model, queuing model, or inventory model; optimal stopping problems for Brownian motion, including the influential McDonald–Siegel investment model; optimal control of Brownian motion via barrier policies, including optimal control of Brownian storage systems; and Brownian models of dynamic inference, also called Brownian learning models or Brownian filtering models.
650		0	_aBrownian motion processes
650		0	_aStochastic processes
776	0	8	_iPrint version: _z9781107018396
856	4	0	_uhttp://dx.doi.org/10.1017/CBO9781139087698
942			_2Dewey Decimal Classification _ceBooks
999			_c37259 _d37259