Applied Probability

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes. Pursuing a pedagogic approach, the content follows a path of increasing complexity, from the simplest random sequences to the advanced stochastic processes. Illustrations are provided from many applied fields, together with connections to ergodic theory, information theory, reliability and insurance. The main content is also complemented by a wealth of examples and exercises with solutions.

Presents a comprehensive course on applied stochastic processes Includes a wealth of exercises with detailed solutions Provides numerous examples in reliability, information theory, production, risk, and other areas Builds a bridge between regular textbooks on probability theory and advanced monographs

Autorentext

Valérie Girardin received her Ph.D. in Probability from the Université Paris-Sud in Orsay, France. She teaches analysis, probability and statistics to various levels of students, including future secondary school teachers in mathematics, future engineers and researchers. Her research interests include diverse aspects of stochastic processes, from theory to applied statistics, with a particular interest in information theory and biology.

Nikolaos Limnios graduated from the Aristotle University of Thessaloniki and Polytechnic School of Thesaloniki, Greece. He received his Ph.D. and his Doctorat d'Etat from the Université de Technologie de Compiègne (UTC), France, where he is now a full professor. He teaches probability, statistics and stochastic processes to future engineers. His research interests in stochastic processes and statistics include Markov, semi-Markov processes, branching processes, random evolutions and their applications in biology, reliability, earthquake, population evolutions, among other topics.

Inhalt
Preface.- Independent Random Sequences.- Conditions and Martingales.- Markov Chains.- Continuous Time Stochastic Processes.- Markov and Semi-Markov Processes.- Further Reading.-