A wide variety of processes occur on multiple scales, either
naturally or as a consequence of measurement. This book contains
methodology for the analysis of data that arise from such
multiscale processes. The book brings together a number of recent
developments and makes them accessible to a wider audience. Taking
a Bayesian approach allows for full accounting of uncertainty, and
also addresses the delicate issue of uncertainty at multiple
scales. The Bayesian approach also facilitates the use of knowledge
from prior experience or data, and these methods can handle
different amounts of prior knowledge at different scales, as often
occurs in practice.
The book is aimed at statisticians, applied mathematicians, and
engineers working on problems dealing with multiscale processes in
time and/or space, such as in engineering, finance, and
environmetrics. The book will also be of interest to those working
on multiscale computation research. The main prerequisites are
knowledge of Bayesian statistics and basic Markov chain Monte Carlo
methods. A number of real-world examples are thoroughly analyzed in
order to demonstrate the methods and to assist the readers in
applying these methods to their own work. To further assist
readers, the authors are making source code (for R) available for
many of the basic methods discussed herein.
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