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MML clustering of multistate, Poisson, von Mises circular and Gaussian distributions
 Statistics Computing
, 2000
"... Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also statistically consistent and efficient. We provide a brief overview of MML inductive inference ..."
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Cited by 39 (12 self)
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Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also statistically consistent and efficient. We provide a brief overview of MML inductive inference
Minimum Message Length Autoregressive Model Order Selection
 International Conference on Intelligent Sensing and Information Processing (ICISIP
, 2004
"... We derive a Minimum Message Length (MML) estimator for stationary and nonstationary autoregressive models using the Wallace and Freeman (1987) approximation. The MML estimator’s model selection performance is empirically compared with AIC, AICc, BIC and HQ in a Monte Carlo experiment by uniformly sa ..."
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Cited by 13 (10 self)
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We derive a Minimum Message Length (MML) estimator for stationary and nonstationary autoregressive models using the Wallace and Freeman (1987) approximation. The MML estimator’s model selection performance is empirically compared with AIC, AICc, BIC and HQ in a Monte Carlo experiment by uniformly sampling from the autoregressive stationarity region. Generally applicable, uniform priors are used on the coefficients, model order and log σ 2 for the MML estimator. The experimental results show the MML estimator to have the best overall average mean squared prediction error and best ability to choose the true model order.
Univariate Polynomial Inference by Monte Carlo Message Length Approximation
 in Int. Conf. Machine Learning
, 2002
"... We apply the Message from Monte Carlo (MMC) algorithm to inference of univariate polynomials. MMC is an algorithm for point estimation from a Bayesian posterior sample. ..."
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Cited by 11 (5 self)
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We apply the Message from Monte Carlo (MMC) algorithm to inference of univariate polynomials. MMC is an algorithm for point estimation from a Bayesian posterior sample.
A Preliminary MML Linear Classifier using Principal Components for Multiple Classes
"... In this paper we improve on the supervised classification method developed in Kornienko et al. (2002) by the introduction of Principal Components Analysis to the inference process. We also extend the classifier from dealing with binomial (twoclass) problems only to multinomial (multiclass) problem ..."
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Cited by 2 (2 self)
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In this paper we improve on the supervised classification method developed in Kornienko et al. (2002) by the introduction of Principal Components Analysis to the inference process. We also extend the classifier from dealing with binomial (twoclass) problems only to multinomial (multiclass) problems. The application to which the MML criterion has been applied in this paper is the classification of objects via a linear hyperplane, where the objects are able to come from any multiclass distribution. The inclusion of Principal Component Analysis to the original inference scheme reduces the bias present in the classifier’s search technique. Such improvements lead to a method which, when compared against three commercial Support Vector Machine (SVM) classifiers on Binary data, was found to be as good as the most successful SVM tested. Furthermore, the new scheme is able to classify objects of a multiclass distribution with just one hyperplane, whereas SVMs require several hyperplanes.
Efficient Linear Regression by Minimum Message Length
"... This paper presents an efficient and general solution to the linear regression problem using the Minimum Message Length (MML) principle. Inference in an MML framework involves optimising a twopart costing function that describes the tradeoff between model complexity and model capability. The MML c ..."
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This paper presents an efficient and general solution to the linear regression problem using the Minimum Message Length (MML) principle. Inference in an MML framework involves optimising a twopart costing function that describes the tradeoff between model complexity and model capability. The MML criterion is integrated into the orthogonal least squares algorithm (MMLOLS) to improve both speed and numerical stability. This allows for the message length to be iteratively updated with the selection of each new regressor, and for potentially problematic regressors to be rejected. The MMLOLS algorithm is subsequently applied to function approximation with univariate polynomials. Empirical results demonstrate superior performance in terms of mean squared prediction error in comparison to several wellknown benchmark criteria. I.