About this book series
Probability Theory and Stochastic Modelling publishes cutting-edge research monographs in probability and its applications, as well as postgraduate-level textbooks that either introduce the reader to new developments in the field, or present a fresh perspective on fundamental topics.
Books in this series are expected to follow rigorous mathematical standards, and all titles will be thoroughly peer-reviewed before being considered for publication.
Probability Theory and Stochastic Modelling covers all aspects of modern probability theory including:
· Gaussian processes
· Markov processes
· Random fields, point processes, and random sets
· Random matrices
· Statistical mechanics, and random media
· Stochastic analysis
· High-dimensional probability
as well as applications that include (but are not restricted to) :
· Branching processes, and other models of population growth
· Communications, and processing networks
· Computational methods in probability theory and stochastic processes, including simulation
· Genetics and other stochastic models in biology and the life sciences
· Information theory, signal processing, and image synthesis
· Mathematical economics and finance
· Statistical methods (e.g. empirical processes, MCMC)
· Statistics for stochastic processes
· Stochastic control, and stochastic differential games
· Stochastic models in operations research and stochastic optimization
· Stochastic models in the physical sciences
Probability Theory and Stochastic Modelling is a merger and continuation of Springer’s Stochastic Modelling and Applied Probability and Probability and Its Applications series.
- Electronic ISSN
- 2199-3149
- Print ISSN
- 2199-3130
- Editor-in-Chief
-
- Peter W. Glynn,
- Andreas E. Kyprianou,
- Yves Le Jan,
- Kavita Ramanan
Book titles in this series
Abstracted and indexed in
-
- Mathematical Reviews
- SCOPUS
- zbMATH