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Stochastic disorder problems

Shiryaev, Albert N.

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Stochastic disorder problems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic disorder problems/ by Albert N. Shiryaev.
作者:	Shiryaev, Albert N.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xix, 397 p. :ill., digital ;24 cm.
內容註:	Preface -- Introduction -- Probabilistic-Statistical Models in Quickest Detection Problems. Discrete and Continuous Time -- Basic Settings and Solutions of Quickest Detection Problems. Discrete Time -- Optimal Stopping Times. General Theory for the Discrete-Time Case -- Optimal Stopping Rules. General Theory for the Discrete-Time Case in the Markov Representation -- Optimal Stopping Rules. General Theory for the Continuous-Time Case -- Basic Formulations and Solutions of Quickest Detection Problems. Continuous-Time. Models with Brownian motion -- Multi-Stage Quickest Detection of Breakdown of a Stationary Regime. Model with Brownian Motion -- Disorder on Filtered Probability Spaces -- Bayesian and Variational Problems of Hypothesis Testing. Brownian Motion Models -- Applications to Financial Mathematics -- References -- Term Index -- Notation Index.
Contained By:	Springer eBooks
標題:	Stochastic analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-01526-8
ISBN:	9783030015268

Stochastic disorder problems
Shiryaev, Albert N.

Stochastic disorder problems[electronic resource] /by Albert N. Shiryaev. - Cham :Springer International Publishing :2019. - xix, 397 p. :ill., digital ;24 cm. - Probability theory and stochastic modelling,v.932199-3130 ;. - Probability theory and stochastic modelling ;v.93..

Preface -- Introduction -- Probabilistic-Statistical Models in Quickest Detection Problems. Discrete and Continuous Time -- Basic Settings and Solutions of Quickest Detection Problems. Discrete Time -- Optimal Stopping Times. General Theory for the Discrete-Time Case -- Optimal Stopping Rules. General Theory for the Discrete-Time Case in the Markov Representation -- Optimal Stopping Rules. General Theory for the Continuous-Time Case -- Basic Formulations and Solutions of Quickest Detection Problems. Continuous-Time. Models with Brownian motion -- Multi-Stage Quickest Detection of Breakdown of a Stationary Regime. Model with Brownian Motion -- Disorder on Filtered Probability Spaces -- Bayesian and Variational Problems of Hypothesis Testing. Brownian Motion Models -- Applications to Financial Mathematics -- References -- Term Index -- Notation Index.

This monograph focuses on those stochastic quickest detection tasks in disorder problems that arise in the dynamical analysis of statistical data. These include quickest detection of randomly appearing targets, of spontaneously arising effects, and of arbitrage (in financial mathematics) There is also currently great interest in quickest detection methods for randomly occurring 'intrusions' in information systems and in the design of defense methods against cyber-attacks. The author shows that the majority of quickest detection problems can be reformulated as optimal stopping problems where the stopping time is the moment the occurrence of 'disorder' is signaled. Thus, considerable attention is devoted to the general theory of optimal stopping rules, and to its concrete problem-solving methods. The exposition covers both the discrete time case, which is in principle relatively simple and allows step-by-step considerations, and the continuous-time case, which often requires more technical machinery such as martingales, supermartingales, and stochastic integrals. There is a focus on the well-developed apparatus of Brownian motion, which enables the exact solution of many problems. The last chapter presents applications to financial markets. Researchers and graduate students interested in probability, decision theory and statistical sequential analysis will find this book useful.

ISBN: 9783030015268

Standard No.: 10.1007/978-3-030-01526-8doiSubjects--Topical Terms:

533923
Stochastic analysis.

LC Class. No.: QA274.2

Dewey Class. No.: 519.22

Stochastic disorder problems
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505 0 $a Preface -- Introduction -- Probabilistic-Statistical Models in Quickest Detection Problems. Discrete and Continuous Time -- Basic Settings and Solutions of Quickest Detection Problems. Discrete Time -- Optimal Stopping Times. General Theory for the Discrete-Time Case -- Optimal Stopping Rules. General Theory for the Discrete-Time Case in the Markov Representation -- Optimal Stopping Rules. General Theory for the Continuous-Time Case -- Basic Formulations and Solutions of Quickest Detection Problems. Continuous-Time. Models with Brownian motion -- Multi-Stage Quickest Detection of Breakdown of a Stationary Regime. Model with Brownian Motion -- Disorder on Filtered Probability Spaces -- Bayesian and Variational Problems of Hypothesis Testing. Brownian Motion Models -- Applications to Financial Mathematics -- References -- Term Index -- Notation Index.
520 $a This monograph focuses on those stochastic quickest detection tasks in disorder problems that arise in the dynamical analysis of statistical data. These include quickest detection of randomly appearing targets, of spontaneously arising effects, and of arbitrage (in financial mathematics) There is also currently great interest in quickest detection methods for randomly occurring 'intrusions' in information systems and in the design of defense methods against cyber-attacks. The author shows that the majority of quickest detection problems can be reformulated as optimal stopping problems where the stopping time is the moment the occurrence of 'disorder' is signaled. Thus, considerable attention is devoted to the general theory of optimal stopping rules, and to its concrete problem-solving methods. The exposition covers both the discrete time case, which is in principle relatively simple and allows step-by-step considerations, and the continuous-time case, which often requires more technical machinery such as martingales, supermartingales, and stochastic integrals. There is a focus on the well-developed apparatus of Brownian motion, which enables the exact solution of many problems. The last chapter presents applications to financial markets. Researchers and graduate students interested in probability, decision theory and statistical sequential analysis will find this book useful.
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