The Minimum Description Length Principle and Its Applications in Statistical Learning and Signal Processing
Will be published approx. on 5. August 2018
Book
Hardback
304 pages
978-1-4987-3978-8 (ISBN)
Description
This book discusses the minimum description length (MDL) principle and its applications to statistical learning and signal/image processing. The authors have extensive experience in applying MDL to solve many scientific problems, and will apply this powerful methodology to a wider research community. There are three books and an edited volume in the market about MDL. All of them discuss the topic at a more fundamental and philosophical level, and at times could be hard for beginners to follow. This book aims to make MDL more accessible for the readers and stresses on the application side.
More details
Persons
Thomas C.M. Lee is Professor of Statistics at the University of California, Davis. His research interests include nonparametric and semiparametric modeling, statistical image and signal processing, and statistical applications in other scientific disciplines. Professor Lee is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected Senior Member of the IEEE. He has served as an associate editor for Bernoulli, the Journal of Computational and Graphical Statistics and Statistica Sinica. Currently he is the editor for the Journal of Computational and Graphical Statistics. Raymond K. W. Wong is Assistant Professor of Statistics at Iowa State University with a Ph.D. from the University of California, Davis.
Content
Introduction. Some Results from Coding Theory. The Minimum Description Length Principle. Other Forms of MDL. Some Classical Applications. Nonparametric Regression, Time Series Signals. Image Processing. Large "p" Small "n" Problems.