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Discrete-Time Semi-Markov Random Evolutions and Their Applications

Über 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 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.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9783031334283
  • Einband:
  • Gebundene Ausgabe
  • Seitenzahl:
  • 216
  • Veröffentlicht:
  • 25 Juli 2023
  • Ausgabe:
  • 23001
  • Abmessungen:
  • 160x18x241 mm.
  • Gewicht:
  • 494 g.
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Beschreibung von 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 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.

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