Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of such algorithms. Techniques of functional analysis are used to derive analytical relationships between approximation methods and convergence properties for general classes of algorithms. The volume provides the necessary background in functional analysis and probability theory. Extensive applications to Markov decision processes are presented.
This volume is intended for mathematicians, engineers and computer scientists, who work on learning processes in numerical analysis and are involved with optimization, optimal control, decision analysis and machine learning.
Dr. Almudevar is Associate Professor of Biostatistics and Computational Biology at the University of Rochester Medical Centre, NY, USA. He has long-standing experience in genetics and bioinformatics, especially in the area of graphical modeling, with applications to cellular networks and population biology, as well as a more general interest in optimization and control theory, particularly in the area of Markov decision processes which has fed into this book.
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