Probability Theory and Stochastic Modelling
The Probability Theory and Stochastic Modelling series is a merger and continuation of Springer’s two well established series Stochastic Modelling and Applied Probability and Probability and Its Applications. It publishes research monographs that make a significant contribution to probability theory or an applications domain in which advanced probability methods are fundamental. Books in this series are expected to follow rigorous mathematical standards, while also displaying the expository quality necessary to make them useful and accessible to advanced students as well as researchers. The series 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 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 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 - Stochastic models in operations research and stochastic optimization - Stochastic models in the physical sciences.
18 Volumes from 1998 – 2017Browse All Volumes