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A Course in Stochastic Processes ― Stochastic Models and Statistical Inference
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This volume is an introduction to stochastic processes and their statistics. Basic stochastic processes are developed from real world situations to the need for generating mathematical models, while at the same time students learn to apply theoretical models.
The lessons cover basic stochastic processes such as Poisson processes, Markov chains, random walks, renewal theory, queuing theory, ARMA models, martingales, Brownian motion and diffusion processes. The statistical topics treated include the basic aspects of statistics of point processes, stationary processes and diffusion processes.
This textbook will be useful for one-semester courses at graduate level to students of mathematics, statistics, computer science, electrical and industrial engineering and economics.
The lessons cover basic stochastic processes such as Poisson processes, Markov chains, random walks, renewal theory, queuing theory, ARMA models, martingales, Brownian motion and diffusion processes. The statistical topics treated include the basic aspects of statistics of point processes, stationary processes and diffusion processes.
This textbook will be useful for one-semester courses at graduate level to students of mathematics, statistics, computer science, electrical and industrial engineering and economics.
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