NML computation algorithms for tree-structured multinomial Bayesian networks
EURASIP Journal on Bioinformatics and Systems Biology
Inference of gene regulatory networks based on a universal minimum description length
EURASIP Journal on Bioinformatics and Systems Biology
Artificial General Intelligence through Large-Scale, Multimodal Bayesian Learning
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Structural break estimation of noisy sinusoidal signals
Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Approximation of the two-part MDL code
IEEE Transactions on Information Theory
Universal models for the exponential distribution
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Entropy and mutual information can improve fitness evaluation in coevolution of neural networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
MML Invariant Linear Regression
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Model selection by sequentially normalized least squares
Journal of Multivariate Analysis
Fast NML computation for Naive Bayes models
DS'07 Proceedings of the 10th international conference on Discovery science
Learning locally minimax optimal Bayesian networks
International Journal of Approximate Reasoning
Variance-component based sparse signal reconstruction and model selection
IEEE Transactions on Signal Processing
Information distance based fitness and diversity metrics
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A signal processing view on packet sampling and anomaly detection
INFOCOM'10 Proceedings of the 29th conference on Information communications
Selection of statistical thresholds in graphical models
EURASIP Journal on Bioinformatics and Systems Biology
Effective complexity and its relation to logical depth
IEEE Transactions on Information Theory
Transactions on rough sets XII
Gauging the value of good data: Informational embodiment quantification
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
ITCH: information-theoretic cluster hierarchies
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Gaussian clusters and noise: an approach based on the minimum description length principle
DS'10 Proceedings of the 13th international conference on Discovery science
Scalable clustering of news search results
Proceedings of the fourth ACM international conference on Web search and data mining
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Information, Divergence and Risk for Binary Experiments
The Journal of Machine Learning Research
Real-time change-point detection using sequentially discounting normalized maximum likelihood coding
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
International Journal of Approximate Reasoning
Dependency clustering across measurement scales
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Summarization-based mining bipartite graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Description length and dimensionality reduction in functional data analysis
Computational Statistics & Data Analysis
Summarizing categorical data by clustering attributes
Data Mining and Knowledge Discovery
Project dynamics and emergent complexity
Computational & Mathematical Organization Theory
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No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines "noise" as the incompressible part in the data without useful information. 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.