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
Table des matières
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.
A propos de l’auteur
R. Cairoli and Robert C. Dalang are the authors of Sequential Stochastic Optimization, published by Wiley.
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