Markov Renewal and Piecewise Deterministic Processes

AvChristiane Cocozza-Thivent

Häftad, Engelska, 2022

Del i serien Probability Theory and Stochastic Modelling

1 384 kr

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Beskrivning

This book is aimed at researchers, graduate students and engineers who would like to be initiated to Piecewise Deterministic Markov Processes (PDMPs). semi-Markov processes completed to become Markov processes. The last chapter is an opening on processes for which the deterministic flow of the PDMP is replaced with a Markov process.

Produktinformation

Utgivningsdatum:2022-06-10
Mått:155 x 235 x 15 mm
Vikt:411 g
Format:Häftad
Språk:Engelska
Serie:Probability Theory and Stochastic Modelling
Antal sidor:252
Förlag:Springer Nature Switzerland AG
ISBN:9783030704490

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Mer om författaren

Christiane Cocozza-Thivent was trained in probability theory at Laboratoire de Probabilités of Université Pierre et Marie Curie (Paris VI University). In 1983 she defended her State doctorate whose main subject was infinite particle systems.In 1986 she became full professor at Université de Technologie de Compiègne (France) where she supervised theses in image processing, speech recognition and reliability, in partnership with industrial companies. In 1991 she was appointed at Université Paris-Est Marne-la-Vallée (now Gustave Eiffel University) where she initiated and was in charge of the applied mathematics cursus.Her research interests include stochastic models in relation with industrial problems and especially with predictive reliability.

Recensioner i media

“Reading the book is instructive and enjoyable. The author, with a rich experience in the field, proposes us new perspectives on the Markov processes and their applications.” (Eugen Paltanea, zbMATH 1483.60001, 2022)

Innehållsförteckning

Tools.- Markov renewal processes and related processes.- First steps with PDMP.- Hitting time distribution.- Intensity of some marked point pocesses.- Generalized Kolmogorov equations.- A martingale approach.- Stability.- Numerical methods.- Switching Processes.- Tools.- Interarrival distribution with several Dirac measures.- Algorithm convergence's proof.