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.
Major topics covered in Sequential Stochastic Optimization include:
* Fundamental notions, such as essential supremum, stopping points, accessibility, martingales and supermartingales indexed by INd
* Conditions which ensure the integrability of certain suprema ofpartial sums of arrays of independent random variables
* The general theory of optimal stopping for processes indexed by Ind
* Structural properties of information flows
* Sequential sampling and the theory of optimal sequential control
* Multi-armed bandits, Markov chains and optimal switching betweenrandom walks
表中的内容
Preliminaries.
Sums of Independent Random Variables.
Optimal Stopping.
Reduction to a Single Dimension.
Accessibility and Filtration Structure.
Sequential Sampling.
Optimal Sequential Control.
Multiarmed Bandits.
The Markovian Case.
Optimal Switching Between Two Random Walks.
Bibliography.
Indexes.
关于作者
R. Cairoli and Robert C. Dalang are the authors of Sequential Stochastic Optimization, published by Wiley.
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