| Summary: | Sequential Stochastic Optimization provides mathematicians andapplied researchers with a well-developed framework in whichstochastic optimization problems can be formulated and solved.Offering much material that is either new or has never beforeappeared in book form, it lucidly presents a unified theory ofoptimal stopping and optimal sequential control of stochasticprocesses. This book has been carefully organized so that littleprior knowledge of the subject is assumed; its only prerequisitesare a standard graduate course in probability theory and somefamiliarity with discrete-parameter martingales.<br> <br> Major topics covered in Sequential Stochastic Optimization include:<br> * Fundamental notions, such as essential supremum, stopping points,accessibility, martingales and supermartingales indexed by INd<br> * Conditions which ensure the integrability of certain suprema ofpartial sums of arrays of independent random variables<br> * The general theory of optimal stopping for processes indexed byInd<br> * Structural properties of information flows<br> * Sequential sampling and the theory of optimal sequential control<br> * Multi-armed bandits, Markov chains and optimal switching betweenrandom walks
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