000			02331nam a22001817a 4500
008			160105b2013 xxu||||| |||| 00| 0 eng d
020			_a9781439872062
082			_a519.2 ROT-V
100			_aRotar, Vladimir I.
245			_aProbability and stochastic modeling / _cVladimir I Rotar
260			_aBoca Raton _bCRC Press _c2013
300			_a490 p.
365			_aINR _b995.00
500			_a "Preface: This book is intended as a first course in probability with an emphasis on stochastic modeling. Distinctive features of the book concern its contents and format as well. The Contents. The exposition is rigorous; with rare exceptions, all assertions are proven; almost every topic found in a traditional introductory probability course is covered. On the other hand, the book pays substantial attention to stochastic modeling, which is atypical for first-course textbooks on Probability. Cases in point are Markov chains, birth-death processes (including queuing processes), reliability models, and other topics, both theoretical and applied. We also consider a number of concrete models (for example, a model of financial markets, or the principal components scheme); sometimes, even with real world data. The goal here is not to teach particular models or numerical methods but rather help the student to better appreciate general concepts and theoretical results, and demonstrate practical possibilities and restrictions of different approaches under consideration. The same concerns examples and exercises with use of Excel. Besides the traditional material, we also pay attention to topics usually skipped (or almost skipped) in introductory courses, though in the author's opinion, they are becoming increasingly important. In particular, this concerns martingales, classification of dependency structures, and risk evaluation. The format of the book. The material is presented in the form of two nested "routes". Route 1 contains the basic material designed for a one semester course. This material is self-contained and has a moderate level of difficulty. Route 2 contains all of Route 1, offers a more complete exposition, and is suited for a two-semester course or self-study"--
650			_aProbabilities
650			_aStochastic models
650			_aMATHEMATICS -- Probability & Statistics -- Bayesian Analysis.
999			_c22465 _d22465