000  03760nam a22005895i 4500  

001  9780387268712  
003  DEHe213  
005  20180115171351.0  
007  cr nn 008mamaa  
008  100301s2005 xxu s  0eng d  
020 
_a9780387268712 _99780387268712 

024  7 
_a10.1007/b138226 _2doi 

050  4  _aQA273.A1274.9  
050  4  _aQA274274.9  
072  7 
_aPBT _2bicssc 

072  7 
_aPBWL _2bicssc 

072  7 
_aMAT029000 _2bisacsh 

082  0  4 
_a519.2 _223 
100  1 
_aYin, G. George. _eauthor. 

245  1  0 
_aDiscreteTime Markov Chains _h[electronic resource] : _bTwoTimeScale Methods and Applications / _cby G. George Yin, Qing Zhang. 
264  1 
_aNew York, NY : _bSpringer New York, _c2005. 

300 
_aXX, 347 p. _bonline resource. 

336 
_atext _btxt _2rdacontent 

337 
_acomputer _bc _2rdamedia 

338 
_aonline resource _bcr _2rdacarrier 

347 
_atext file _bPDF _2rda 

490  1 
_aStochastic Modelling and Applied Probability, Applications of Mathematics, _x01724568 ; _v55 

505  0  _aPrologue and Preliminaries  Introduction, Overview, and Examples  Mathematical Preliminaries  Asymptotic Properties  Asymptotic Expansions  Occupation Measures  Exponential Bounds  Interim Summary and Extensions  Applications  Stability of Dynamic Systems  Filtering  Markov Decision Processes  LQ Controls  MeanVariance Controls  Production Planning  Stochastic Approximation.  
520  _aFocusing on discretetimescale Markov chains, the contents of this book are an outgrowth of some of the authors' recent research. The motivation stems from existing and emerging applications in optimization and control of complex hybrid Markovian systems in manufacturing, wireless communication, and financial engineering. Much effort in this book is devoted to designing system models arising from these applications, analyzing them via analytic and probabilistic techniques, and developing feasible computational algorithms so as to reduce the inherent complexity. This book presents results including asymptotic expansions of probability vectors, structural properties of occupation measures, exponential bounds, aggregation and decomposition and associated limit processes, and interface of discretetime and continuoustime systems. One of the salient features is that it contains a diverse range of applications on filtering, estimation, control, optimization, and Markov decision processes, and financial engineering. This book will be an important reference for researchers in the areas of applied probability, control theory, operations research, as well as for practitioners who use optimization techniques. Part of the book can also be used in a graduate course of applied probability, stochastic processes, and applications.  
650  0  _aMathematics.  
650  0  _aOperations research.  
650  0  _aDecision making.  
650  0  _aApplied mathematics.  
650  0  _aEngineering mathematics.  
650  0  _aProbabilities.  
650  0  _aControl engineering.  
650  0  _aRobotics.  
650  0  _aMechatronics.  
650  1  4  _aMathematics. 
650  2  4  _aProbability Theory and Stochastic Processes. 
650  2  4  _aControl, Robotics, Mechatronics. 
650  2  4  _aOperation Research/Decision Theory. 
650  2  4  _aApplications of Mathematics. 
700  1 
_aZhang, Qing. _eauthor. 

710  2  _aSpringerLink (Online service)  
773  0  _tSpringer eBooks  
776  0  8 
_iPrinted edition: _z9780387219486 
830  0 
_aStochastic Modelling and Applied Probability, Applications of Mathematics, _x01724568 ; _v55 

856  4  0  _uhttp://dx.doi.org/10.1007/b138226 
912  _aZDB2SMA  
999 
_c369305 _d369305 