Probability Theory and Stochastic Modelling

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.