Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few.
The author is an Honorary Doctor and Professor Emeritus of the Technical University of Tampere, Finland, a Fellow of Helsinki Institute for Information Technology, and visiting Professor in the
Computer Learning Research Center of University of London, Holloway, England. He is also a Foreign Member of Finland's Academy of Science and Letters, an Associate Editor of IMA Journal of Mathematical Control and Information and of EURASIP Journal on Bioinformatics and Systems Biology. He is also a former Associate Editor of Source Coding of IEEE Transactions on Information Theory.
The author is the recipient of the IEEE Information Theory Society's 1993 Richard W. Hamming medal for fundamental contributions to information theory, statistical inference, control theory, and the theory of complexity; the Information Theory Society's Golden Jubilee Award in 1998 for Technological Innovation for inventing Arithmetic Coding; and the 2006 Kolmogorov medal by University of London. He has also received an IBM Corporate Award for the MDL and PMDL Principles in 1991, and two best paper awards.
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