000			02141cam a2200277zu 4500
001			45002583
003			FRCYB45002583
005			20251020123209.0
006			m o d
007			cr un
008			251020s2008 fr | o|||||0|0|||eng d
020			_a9780123744517
035			_aFRCYB45002583
040			_aFR-PaCSA _ben _c _erda
100	1		_aIbe, Oliver
245	0	1	_aMarkov Processes for Stochastic Modeling _c['Ibe, Oliver']
264		1	_bElsevier Science _c2008
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aIbe, Oliver
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/45002583 _qtext/html _a
520			_aMarkov processes are used to model systems with limited memory. They are used in many areas including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. This book, which is written for upper level undergraduate and graduate students, and researchers, presents a unified presentation of Markov processes. In addition to traditional topics such as Markovian queueing system, the book discusses such topics as continuous-time random walk,correlated random walk, Brownian motion, diffusion processes, hidden Markov models, Markov random fields, Markov point processes and Markov chain Monte Carlo. Continuous-time random walk is currently used in econophysics to model the financial market, which has traditionally been modelled as a Brownian motion. Correlated random walk is popularly used in ecological studies to model animal and insect movement. Hidden Markov models are used in speech analysis and DNA sequence analysis while Markov random fields and Markov point processes are used in image analysis. Thus, the book is designed to have a very broad appeal.
999			_c1553361 _d1553361