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Stationary Processes and Discrete Parameter Markov Processes (Paperback, 1st ed. 2022)
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Stationary Processes and Discrete Parameter Markov Processes (Paperback, 1st ed. 2022)
Series: Graduate Texts in Mathematics, 293
Expected to ship within 9 - 15 working days
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This textbook explores two distinct stochastic processes that
evolve at random: weakly stationary processes and discrete
parameter Markov processes. Building from simple examples, the
authors focus on developing context and intuition before
formalizing the theory of each topic. This inviting approach
illuminates the key ideas and computations in the proofs, forming
an ideal basis for further study. After recapping the essentials
from Fourier analysis, the book begins with an introduction to the
spectral representation of a stationary process. Topics in ergodic
theory follow, including Birkhoff's Ergodic Theorem and an
introduction to dynamical systems. From here, the Markov property
is assumed and the theory of discrete parameter Markov processes is
explored on a general state space. Chapters cover a variety of
topics, including birth-death chains, hitting probabilities and
absorption, the representation of Markov processes as iterates of
random maps, and large deviation theory for Markov processes. A
chapter on geometric rates of convergence to equilibrium includes a
splitting condition that captures the recurrence structure of
certain iterated maps in a novel way. A selection of special topics
concludes the book, including applications of large deviation
theory, the FKG inequalities, coupling methods, and the Kalman
filter. Featuring many short chapters and a modular design, this
textbook offers an in-depth study of stationary and discrete-time
Markov processes. Students and instructors alike will appreciate
the accessible, example-driven approach and engaging exercises
throughout. A single, graduate-level course in probability is
assumed.
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