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Some additional examples of stochastic processes follow.
Basics of stochastic processes.
Limited to 60. Description: Analysis and modeling of stochastic processes.
Cox, D. R. & Miller, H. D. The Theory of Stochastic Processes (Chapman & Hall, London, 1965).
The resulting spectral densities behave like those of stochastic processes with long memory.
53 "Simulation of Stochastic Processes and Fields for Monte Carlo Simulation Applications: Some Recent Developments".
Reviews time-varying systems, first and second moment representations of stochastic processes, and state-space models.
A study of stochastic processes and estimation, including fundamentals of detection and estimation.
The model is based on a linear combination of stochastic processes.
The theory of stochastic processes attempts to build probability models for phenomena that evolve over time.
Some basic types of stochastic processes include Markov processes, Poisson processes (such as radioactive decay), and time series, with the index variable referring to time.
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