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Books > Science & Mathematics > Mathematics > Applied mathematics > Stochastics
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Complex Stochastic Systems (Paperback)
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Complex Stochastic Systems (Paperback)
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Complex stochastic systems comprises a vast area of research, from
modelling specific applications to model fitting, estimation
procedures, and computing issues. The exponential growth in
computing power over the last two decades has revolutionized
statistical analysis and led to rapid developments and great
progress in this emerging field. In Complex Stochastic Systems,
leading researchers address various statistical aspects of the
field, illustrated by some very concrete applications. A Primer on
Markov Chain Monte Carlo by Peter J. Green provides a wide-ranging
mixture of the mathematical and statistical ideas, enriched with
concrete examples and more than 100 references. Causal Inference
from Graphical Models by Steffen L. Lauritzen explores causal
concepts in connection with modelling complex stochastic systems,
with focus on the effect of interventions in a given system. State
Space and Hidden Markov Models by Hans R. Kunschshows the variety
of applications of this concept to time series in engineering,
biology, finance, and geophysics. Monte Carlo Methods on Genetic
Structures by Elizabeth A. Thompson investigates special complex
systems and gives a concise introduction to the relevant biological
methodology. Renormalization of Interacting Diffusions by Frank den
Hollander presents recent results on the large space-time behavior
of infinite systems of interacting diffusions. Stein's Method for
Epidemic Processes by Gesine Reinert investigates the mean field
behavior of a general stochastic epidemic with explicit bounds.
Individually, these articles provide authoritative, tutorial-style
exposition and recent results from various subjects related to
complex stochastic systems. Collectively, they link these separate
areas of study to form the first comprehensive overview of this
rapidly developing field.
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