Introduces the Minimum Description Length principle for inductive inference, enabling readers to grasp its foundational role in learning and statistics
Covers theoretical foundations and practical applications, including detection of changes, anomalies, and high-dimensional statistical inference
Compares different information criteria to help readers understand their respective standpoints
Written in a systematic, concise, and comprehensive style for easy understanding
Suitable for researchers and graduate students in machine learning, statistics, information theory, and computer science