Results 1 
7 of
7
A tutorial introduction to the minimum description length principle
 in Advances in Minimum Description Length: Theory and Applications. 2005
"... ..."
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 ..."
Abstract

Cited by 32 (10 self)
 Add to MetaCart
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
Suboptimal behavior of Bayes and MDL in classification under misspecification
 COLT
, 2004
"... We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian posterior both remain bounded away from the smallest achievable generalization error. From a Bayesian point of view, the result can be reinterpreted as saying that Bayesian inference can be inconsistent under misspecification, even for countably infinite models. We extensively discuss the result from both a Bayesian and an MDL perspective.
Minimum Message Length Grouping of Ordered Data
 Proceedings of the Eleventh International Conference on Algorithmic Learning Theory (ALT2000), LNAI
, 2000
"... Explicit segmentation is the partitioning of data into homogeneous regions by specifying cutpoints. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Progr ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
Explicit segmentation is the partitioning of data into homogeneous regions by specifying cutpoints. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Programming Algorithm (DPA). Oliver, Baxter and colleagues (1996,1997,1998) have applied the informationtheoretic Minimum Message Length (MML) principle to explicit segmentation. Given a series of multivariate data, approximate it by a piecewise constant function. How many cutpoints are there? What are the means and variances of each segment? Where should the cut points be placed? The simplest model is a single segment. The most complex model has one segment per data point. The best model is generally somewhere between these extremes. Only by considering model complexity can a reasonable inference be made.
ChangePoint Estimation Using New Minimum Message Length Approximations
 Proc. PRICAI
, 2002
"... This paper investigates the coding of changepoints in the informationtheoretic Minimum Message Length (MML) framework. Changepoint coding regions affect model selection and parameter estimation in problems such as time series segmentation and decision trees. The Minimum Message Length (MML) and Mi ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This paper investigates the coding of changepoints in the informationtheoretic Minimum Message Length (MML) framework. Changepoint coding regions affect model selection and parameter estimation in problems such as time series segmentation and decision trees. The Minimum Message Length (MML) and Minimum Description Length (MDL78) approaches to changepoint problems have been shown to perform well by several authors. In this paper we compare some published MML and MDL78 methods and introduce some new MML approximations called `MMLDc' and `MMLDF'. These new approximations are empirically compared with Strict MML (SMML), Fairly Strict MML (FSMML), MML68, the Minimum Expected KullbackLeibler Distance (MEKLD) loss function and MDL78 on a tractable binomial changepoint problem.
2003).The Story of The Hot Hand: Powerful Myth or Powerless Critique
 International Conference on Cognitive Science
, 2003
"... Thomas Gilovich and Amos Tversky famously claimed that the belief in the Hot Hand in basketball is a cognitive illusion, an instance of the belief in the Law of Small Numbers. They supported this claim with a variety of orthodox statistical tests failing to show any clear evidence of a Hot Hand phen ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Thomas Gilovich and Amos Tversky famously claimed that the belief in the Hot Hand in basketball is a cognitive illusion, an instance of the belief in the Law of Small Numbers. They supported this claim with a variety of orthodox statistical tests failing to show any clear evidence of a Hot Hand phenomenon. These researchers neglected to perform any power analysis on this test. Here we show that their belief in having demonstrated the illusory status of the Hot Hand is itself an illustration of the Law of Small Numbers.
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 ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
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.