Technological improvements continue to push back the frontier of
processor speed in modern computers. Unfortunately, the
computational intensity demanded by modern research problems grows
even faster. Parallel computing has emerged as the most successful
bridge to this computational gap, and many popular solutions have
emerged based on its concepts, such as grid computing and massively
parallel supercomputers. The Handbook of Parallel Computing and
Statistics systematically applies the principles of parallel
computing for solving increasingly complex problems in statistics
research.
This unique reference weaves together the principles and
theoretical models of parallel computing with the design, analysis,
and application of algorithms for solving statistical problems.
After a brief introduction to parallel computing, the book explores
the architecture, programming, and computational aspects of
parallel processing. Focus then turns to optimization methods
followed by statistical applications. These applications include
algorithms for predictive modeling, adaptive design, real-time
estimation of higher-order moments and cumulants, data mining,
econometrics, and Bayesian computation. Expert contributors
summarize recent results and explore new directions in these
areas.
Its intricate combination of theory and practical applications
makes the Handbook of Parallel Computing and Statistics an ideal
companion for helping solve the abundance of computation-intensive
statistical problems arising in a variety of fields.
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