Includes bibliographical references (pages [156]-160) and index.

Machine generated contents note: 1. Introduction; 2. Coding; 3. Basics of information; 4. Modeling problem; 5. Other optimality properties; 6. Interval estimation; 7. Hypothesis testing; 8. Denoising; 9. Sequential models; Appendix A. Elements of algorithmic information; Appendix B. Universal prior for integers.

"This book presents a comprehensive and consistent theory of estimation. The framework described leads naturally to a generalized maximum capacity estimator. This approach allows the optimal estimation of real-valued parameters, their number and intervals, as well as providing common ground for explaining the power of these estimators. Beginning with a review of coding and the key properties of information, the author goes on to discuss the techniques of estimation and develops the generalized maximum capacity estimator, based on a new form of Shannon's mutual information and channel capacity. Applications of this powerful technique in hypothesis testing and denoising are described in detail. Offering an original and thought-provoking perspective on estimation theory, Jorma Rissanen's book is of interest to graduate students and researchers in the fields of information theory, probability and statistics, econometrics and finance"--