Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Classification by minimum-message-length inference
ICCI'90 Proceedings of the international conference on Advances in computing and information
The nature of statistical learning theory
The nature of statistical learning theory
An Experimental and Theoretical Comparison of Model SelectionMethods
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
On the Length of Programs for Computing Finite Binary Sequences
Journal of the ACM (JACM)
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Finding overlapping components with MML
Statistics and Computing
Single Factor Analysis in MML Mixture Modelling
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Point Estimation Using the Kullback-Leibler Loss Function and MML
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
The discovery of algorithmic probability: A guide for the programming of true creativity
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
MML Estimation of the Parameters of the Sherical Fisher Distribution
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
MML Clustering of Continuous-Valued Data Using Gaussian and t Distributions
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
MML Inference of Decision Graphs with Multi-way Joins
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Argument Interpretation Using Minimum Message Length
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Feature Selection for Temporal Health Records
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Minimum Message Length Grouping of Ordered Data
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Seneschal: classification and analysis in supervised mixture-modelling
Design and application of hybrid intelligent systems
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Distributed Data Mining System for a Novel Ubiquitous Healthcare Framework
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Distributed Data Mining in a Ubiquitous Healthcare Framework
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
International Journal of Remote Sensing
Linear Time Model Selection for Mixture of Heterogeneous Components
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Information-Theoretic Image Reconstruction and Segmentation from Noisy Projections
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Enhancing MML Clustering Using Context Data with Climate Applications
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Compression and intelligence: social environments and communication
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
A case study in knowledge discovery and elicitation in an intelligent tutoring application
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
On fitting finite dirichlet mixture using ECM and MML
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Distributed data mining on clusters with bayesian mixture modeling
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
MML mixture models of heterogeneous poisson processes with uniform outliers for bridge deterioration
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Minimum message length inference and mixture modelling of inverse gaussian distributions
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Minimum Message Length (MML) is an invariantBayesian point estimation technique which is also statisticallyconsistent and efficient. We provide a brief overview of MMLinductive inference (Wallace C.S. and Boulton D.M. 1968. ComputerJournal, 11: 185–194; Wallace C.S. and FreemanP.R. 1987. J. RoyalStatistical Society (Series B), 49: 240–252; WallaceC.S. and DoweD.L. (1999). Computer Journal), and how it has both aninformation-theoretic and a Bayesian interpretation. We then outlinehow MML is used for statistical parameter estimation, and how the MMLmixture modelling program, Snob (Wallace C.S. and Boulton D.M. 1968.Computer Journal, 11: 185–194; Wallace C.S. 1986.In: Proceedings ofthe Nineteenth Australian Computer Science Conference (ACSC-9), Vol.8, Monash University, Australia, pp. 357–366; Wallace C.S.and Dowe D.L. 1994b.In: Zhang C. et al. (Eds.), Proc. 7th AustralianJoint Conf. on Artif.Intelligence. World Scientific, Singapore, pp. 37–44. Seehttp://www.csse.monash.edu.au/-dld/Snob.html) uses the messagelengths from various parameter estimates to enable it to combineparameter estimation with selection of the number of components andestimation of the relative abundances of the components. The messagelength is (to within a constant) the logarithm of the posteriorprobability (not a posterior density) of the theory. So, theMML theory can also be regarded as the theory with the highestposterior probability. Snob currently assumes that variables areuncorrelated within each component, and permits multi-variate datafrom Gaussian, discrete multi-category (or multi-state ormultinomial), Poisson and von Mises circular distributions, as wellas missing data. Additionally, Snob can do fully-parameterisedmixture modelling, estimating the latent class assignments inaddition to estimating the number of components, the relativeabundances of the parameters and the component parameters. We alsoreport on extensions of Snob for data which has sequential or spatialcorrelations between observations, or correlations betweenattributes.