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15
Minimum Message Length and Kolmogorov Complexity
 Computer Journal
, 1999
"... this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465] ..."
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Cited by 104 (25 self)
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this paper is to describe some of the relationships among the different streams and to try to clarify some of the important differences in their assumptions and development. Other studies mentioning the relationships appear in [1, Section IV, pp. 10381039], [2, sections 5.2, 5.5] and [3, p. 465]
A Nonbehavioural, Computational Extension to the Turing Test
 In International Conference on Computational Intelligence & Multimedia Applications (ICCIMA '98
, 1998
"... We also ask the following question: Given two programs H1 and H2 respectively of lengths l1 and l2, l1! l2, if H1 and H2 perform equally well (to date) on a Turing Test, which, if either, should be preferred for the future? We also set a challenge. If humans can presume intelligence in their ability ..."
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Cited by 33 (18 self)
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We also ask the following question: Given two programs H1 and H2 respectively of lengths l1 and l2, l1! l2, if H1 and H2 perform equally well (to date) on a Turing Test, which, if either, should be preferred for the future? We also set a challenge. If humans can presume intelligence in their ability to set the Turing test, then we issue the additional challenge to researchers to get machines to administer the Turing Test.
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 32 (10 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
Bayes not Bust! Why Simplicity is no Problem for Bayesians
, 2007
"... The advent of formal definitions of the simplicity of a theory has important implications for model selection. But what is the best way to define simplicity? Forster and Sober ([1994]) advocate the use of Akaike’s Information Criterion (AIC), a nonBayesian formalisation of the notion of simplicity. ..."
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Cited by 13 (10 self)
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The advent of formal definitions of the simplicity of a theory has important implications for model selection. But what is the best way to define simplicity? Forster and Sober ([1994]) advocate the use of Akaike’s Information Criterion (AIC), a nonBayesian formalisation of the notion of simplicity. This forms an important part of their wider attack on Bayesianism in the philosophy of science. We defend a Bayesian alternative: the simplicity of a theory is to be characterised in terms of Wallace’s Minimum Message Length (MML). We show that AIC is inadequate for many statistical problems where MML performs well. Whereas MML is always defined, AIC can be undefined. Whereas MML is not known ever to be statistically inconsistent, AIC can be. Even when defined and consistent, AIC performs worse than MML on small sample sizes. MML is statistically invariant under 1to1 reparametrisation, thus avoiding a common criticism of Bayesian approaches. We also show that MML provides answers to many of Forster’s objections to Bayesianism. Hence an important part of the attack on
An MML Classification of Protein Structure that knows about Angles and Sequence
"... this paper we apply a Hidden Markov Model to model the structure of a collection of known proteins. This Markov classi#cation is able to take advantage of information implicit in the order of a sequence of observations and hence is better suited to modelling protein data than a classi#cation model t ..."
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Cited by 10 (5 self)
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this paper we apply a Hidden Markov Model to model the structure of a collection of known proteins. This Markov classi#cation is able to take advantage of information implicit in the order of a sequence of observations and hence is better suited to modelling protein data than a classi#cation model that assumes independence between observations. We use an Minimum Message Length #MML# information measure to evaluate our protein structure model which enables us to #nd the model best supported by the known evidence
MML mixture modelling of multistate, Poisson, von Mises circular and Gaussian distributions
 In Proc. 6th Int. Workshop on Artif. Intelligence and Statistics
, 1997
"... Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an informationtheoretic and a Bayesian interp ..."
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Cited by 8 (5 self)
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Minimum Message Length (MML) is an invariant Bayesian point estimation technique which is also consistent and efficient. We provide a brief overview of MML inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)), and how it has both an informationtheoretic and a Bayesian interpretation. We then outline how MML is used for statistical parameter estimation, and how the MML mixture modelling program, Snob (Wallace and Boulton (1968), Wallace (1986), Wallace and Dowe(1994)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with selection of the number of components. The message length is (to within a constant) the logarithm of the posterior probability of the theory. So, the MML theory can also be regarded as the theory with the highest posterior probability. Snob currently assumes that variables are uncorrelated, and permits multivariate data from Gaussian, discrete multistate, Poisson and von Mises circular dist...
Bayesian Estimation Of The Von Mises Concentration Parameter
 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL WORKSHOP ON MAXIMUM ENTROPY AND BAYESIAN METHODS
"... The von Mises distribution is a maximum entropy distribution. It corresponds to the distribution of an angle of a compass needle in a uniform magnetic field of direction, , with concentration parameter, . The concentration parameter, , is the ratio of the field strength to the temperature of thermal ..."
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Cited by 7 (5 self)
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The von Mises distribution is a maximum entropy distribution. It corresponds to the distribution of an angle of a compass needle in a uniform magnetic field of direction, , with concentration parameter, . The concentration parameter, , is the ratio of the field strength to the temperature of thermal fluctuations. Previously, we obtained a Bayesian estimator for the von Mises distribution parameters using the informationtheoretic MinimumMessage Length (MML) principle. Here, we examine a variety of Bayesian estimation techniques by examining the posterior distribution in both polar and Cartesian coordinates. We compare the MML estimator with these fellow Bayesian techniques, and a range of Classical estimators. We find that the Bayesian estimators outperform the Classical estimators.
MDL and MML: Similarities and Differences (Introduction to Minimum Encoding Inference  Part III)
, 1994
"... This paper continues the introduction to minimum encoding inductive inference given by Oliver and Hand. This series of papers was written with the objective of providing an introduction to this area for statisticians. We describe the message length estimates used in Wallace's Minimum Message Length ..."
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Cited by 6 (0 self)
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This paper continues the introduction to minimum encoding inductive inference given by Oliver and Hand. This series of papers was written with the objective of providing an introduction to this area for statisticians. We describe the message length estimates used in Wallace's Minimum Message Length (MML) inference and Rissanen's Minimum Description Length (MDL) inference. The differences in the message length estimates of the two approaches are explained. The implications of these differences for applications are discussed.
MML and Bayesianism: Similarities and Differences (Introduction to Minimum Encoding Inference  Part II)
, 1994
"... This paper continues the introduction to minimum encoding inference given by Oliver and Hand. This series of papers were written with the objective of providing an introduction to this area for statisticians. We examine the relationship between Bayesianism and Minimum Message Length (MML) inference. ..."
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Cited by 6 (0 self)
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This paper continues the introduction to minimum encoding inference given by Oliver and Hand. This series of papers were written with the objective of providing an introduction to this area for statisticians. We examine the relationship between Bayesianism and Minimum Message Length (MML) inference. We argue that MML augments Bayesian methods by providing a sound Bayesian method for point estimation which is invariant under nonlinear transformations. We explore the issues of invariance of estimators under nonlinear transformations, the role of the Fisher Information matrix in MML inference, and the apparent similarity between MML and the adoption of a Jeffreys' Prior. We then compare MML to an approximate method of Bayesian Model Class Selection. Despite apparent similarities in their expressions, the properties of the two approaches can be different.
Intrinsic Classification by MML—the Snob Program
 Proc. Seventh Australian Joint Conf. Artificial Intelligence
, 1994
"... Abstract: We provide a brief overview ofMinimum Message Length (MML) inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)). We then outline how MML is used for statistical parameter estimation, and how the MML intrinsic classification program, Snob (Wallace and Boulton (1968), ..."
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Cited by 6 (0 self)
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Abstract: We provide a brief overview ofMinimum Message Length (MML) inductive inference (Wallace and Boulton (1968), Wallace and Freeman (1987)). We then outline how MML is used for statistical parameter estimation, and how the MML intrinsic classification program, Snob (Wallace and Boulton (1968), Wallace (1986), Wallace (1990)) uses the message lengths from various parameter estimates to enable it to combine parameter estimation with model selection in intrinsic classification. We mention here the most recent extensions to Snob, permitting Poisson and von Mises circular distributions. We also survey some applications of Snob (albeit briefly), and further provide some documentation on how the user can guide Snob’s search through various models of the given data to try to obtain that model whose message length is a minimum.