Discrete-Time Semi-Markov Random Evolutions and Their Applications
This book extends the theory and applications of random evolutions to semi-Markov random media in discrete time, essentially focusing on semi-Markov chains as switching or driving processes. After giving the definitions of discrete-time semi-Markov chains and random evolutions, it presents the asymp...
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| Автори: | , |
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| Співавтор: | |
| Формат: | Електронний ресурс eКнига |
| Мова: | Англійська |
| Опубліковано: |
Cham :
Springer Nature Switzerland : Imprint: Birkhäuser,
2023.
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| Редагування: | 1st ed. 2023. |
| Серія: | Probability and Its Applications (duplicate),
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| Предмети: | |
| Онлайн доступ: | https://doi.org/10.1007/978-3-031-33429-0 |
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| 100 | 1 | |a Limnios, Nikolaos. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Discrete-Time Semi-Markov Random Evolutions and Their Applications |h [electronic resource] / |c by Nikolaos Limnios, Anatoliy Swishchuk. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Birkhäuser, |c 2023. | |
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| 490 | 1 | |a Probability and Its Applications (duplicate), |x 2297-0398 | |
| 520 | |a This book extends the theory and applications of random evolutions to semi-Markov random media in discrete time, essentially focusing on semi-Markov chains as switching or driving processes. After giving the definitions of discrete-time semi-Markov chains and random evolutions, it presents the asymptotic theory in a functional setting, including weak convergence results in the series scheme, and their extensions in some additional directions, including reduced random media, controlled processes, and optimal stopping. Finally, applications of discrete-time semi-Markov random evolutions in epidemiology and financial mathematics are discussed. This book will be of interest to researchers and graduate students in applied mathematics and statistics, and other disciplines, including engineering, epidemiology, finance and economics, who are concerned with stochastic models of systems. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Stochastic processes. | |
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| 650 | 2 | 4 | |a Applied Probability. |
| 650 | 2 | 4 | |a Dynamical Systems. |
| 650 | 2 | 4 | |a Stochastic Systems and Control. |
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