Results 1  10
of
85
The minimum description length principle in coding and modeling
 IEEE Trans. Inform. Theory
, 1998
"... Abstract — We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized m ..."
Abstract

Cited by 306 (12 self)
 Add to MetaCart
Abstract — We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms. We assess the performance of the minimum description length criterion both from the vantage point of quality of data compression and accuracy of statistical inference. Context tree modeling, density estimation, and model selection in Gaussian linear regression serve as examples. Index Terms—Complexity, compression, estimation, inference, universal modeling.
Model Selection and the Principle of Minimum Description Length
 Journal of the American Statistical Association
, 1998
"... This paper reviews the principle of Minimum Description Length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This ..."
Abstract

Cited by 147 (5 self)
 Add to MetaCart
This paper reviews the principle of Minimum Description Length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This approach began with Kolmogorov's theory of algorithmic complexity, matured in the literature on information theory, and has recently received renewed interest within the statistics community. In the pages that follow, we review both the practical as well as the theoretical aspects of MDL as a tool for model selection, emphasizing the rich connections between information theory and statistics. At the boundary between these two disciplines, we find many interesting interpretations of popular frequentist and Bayesian procedures. As we will see, MDL provides an objective umbrella under which rather disparate approaches to statistical modeling can coexist and be compared. We illustrate th...
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
 Machine Learning
, 1994
"... In this paper we study a Bayesian or averagecase model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the l ..."
Abstract

Cited by 109 (12 self)
 Add to MetaCart
In this paper we study a Bayesian or averagecase model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the learner, and to smoothly unite in a common framework the popular statistical physics and VC dimension theories of learning curves. To achieve this, we undertake a systematic investigation and comparison of two fundamental quantities in learning and information theory: the probability of an incorrect prediction for an optimal learning algorithm, and the Shannon information gain. This study leads to a new understanding of the sample complexity of learning in several existing models. 1 Introduction Consider a simple concept learning model in which the learner attempts to infer an unknown target concept f , chosen from a known concept class F of f0; 1gvalued functions over an instance space X....
InformationTheoretic Determination of Minimax Rates of Convergence
 Ann. Stat
, 1997
"... In this paper, we present some general results determining minimax bounds on statistical risk for density estimation based on certain informationtheoretic considerations. These bounds depend only on metric entropy conditions and are used to identify the minimax rates of convergence. ..."
Abstract

Cited by 93 (18 self)
 Add to MetaCart
In this paper, we present some general results determining minimax bounds on statistical risk for density estimation based on certain informationtheoretic considerations. These bounds depend only on metric entropy conditions and are used to identify the minimax rates of convergence.
A Bayesian/information theoretic model of learning to learn via multiple task sampling
 Machine Learning
, 1997
"... Abstract. A Bayesian model of learning to learn by sampling from multiple tasks is presented. The multiple tasks are themselves generated by sampling from a distribution over an environment of related tasks. Such an environment is shown to be naturally modelled within a Bayesian context by the conce ..."
Abstract

Cited by 78 (2 self)
 Add to MetaCart
Abstract. A Bayesian model of learning to learn by sampling from multiple tasks is presented. The multiple tasks are themselves generated by sampling from a distribution over an environment of related tasks. Such an environment is shown to be naturally modelled within a Bayesian context by the concept of an objective prior distribution. It is argued that for many common machine learning problems, although in general we do not know the true (objective) prior for the problem, we do have some idea of a set of possible priors to which the true prior belongs. It is shown that under these circumstances a learner can use Bayesian inference to learn the true prior by learning sufficiently many tasks from the environment. In addition, bounds are given on the amount of information required to learn a task when it is simultaneously learnt with several other tasks. The bounds show that if the learner has little knowledge of the true prior, but the dimensionality of the true prior is small, then sampling multiple tasks is highly advantageous. The theory is applied to the problem of learning a common feature set or equivalently a lowdimensionalrepresentation (LDR) for an environment of related tasks.
Strong Optimality of the Normalized ML Models as Universal Codes
 IEEE Transactions on Information Theory
, 2000
"... We show that the normalized maximum likelihood (NML) distribution as a universal code for a parametric class of models is closest to the negative logarithm of the maximized likelihood in the mean code length distance, where the mean is taken with respect to the worst case model inside or outside ..."
Abstract

Cited by 59 (7 self)
 Add to MetaCart
We show that the normalized maximum likelihood (NML) distribution as a universal code for a parametric class of models is closest to the negative logarithm of the maximized likelihood in the mean code length distance, where the mean is taken with respect to the worst case model inside or outside the parametric class. We strengthen this result by showing that the same minmax bound results even when the data generating models are restricted to be most `benevolent' in minimizing the mean of the negative logarithm of the maximized likelihood. Further, we show for the class of exponential models that the bound cannot be beaten in essence by any code except when the mean is taken with respect to the most benevolent data generating models in a set of vanishing size. These results allow us to decompose the data into two parts, the first having all the useful information that can be extracted with the parametric models and the rest which has none. We also show that, if we change Ak...
Hypothesis Selection and Testing by the MDL Principle
 The Computer Journal
, 1998
"... ses where the variance is known or taken as a parameter. 1. INTRODUCTION Although the term `hypothesis' in statistics is synonymous with that of a probability `model' as an explanation of data, hypothesis testing is not quite the same problem as model selection. This is because usually a particul ..."
Abstract

Cited by 58 (3 self)
 Add to MetaCart
ses where the variance is known or taken as a parameter. 1. INTRODUCTION Although the term `hypothesis' in statistics is synonymous with that of a probability `model' as an explanation of data, hypothesis testing is not quite the same problem as model selection. This is because usually a particular hypothesis, called the `null hypothesis', has already been selected as a favorite model and it will be abandoned in favor of another model only when it clearly fails to explain the currently available data. In model selection, by contrast, all the models considered are regarded on the same footing and the objective is simply to pick the one that best explains the data. For the Bayesians certain models may be favored in terms of a prior probability, but in the minimum description length (MDL) approach to be outlined below, prior knowledge of any kind is to be used in selecting the tentative models, which in the end, unlike in the Bayesians' case, can and will be fitted to data
Statistical Inference, Occam’s Razor, and Statistical Mechanics on the Space of Probability Distributions
, 1997
"... The task of parametric model selection is cast in terms of a statistical mechanics on the space of probability distributions. Using the techniques of lowtemperature expansions, I arrive at a systematic series for the Bayesian posterior probability of a model family that significantly extends known ..."
Abstract

Cited by 55 (3 self)
 Add to MetaCart
The task of parametric model selection is cast in terms of a statistical mechanics on the space of probability distributions. Using the techniques of lowtemperature expansions, I arrive at a systematic series for the Bayesian posterior probability of a model family that significantly extends known results in the literature. In particular, I arrive at a precise understanding of how Occam’s razor, the principle that simpler models should be preferred until the data justify more complex models, is automatically embodied by probability theory. These results require a measure on the space of model parameters and I derive and discuss an interpretation of Jeffreys ’ prior distribution as a uniform prior over the distributions indexed by a family. Finally, I derive a theoretical index of the complexity of a parametric family relative to some true distribution that I call the razor of the model. The form of the razor immediately suggests several interesting questions in the theory of learning that can be studied using the techniques of statistical mechanics.
A Vector Quantization Approach to Universal Noiseless Coding and Quantization
 IEEE Trans. Inform. Theory
, 1996
"... AbstractA twostage code is a block code in which each block of data is coded in two stages: the first stage codes the identity of a block code among a collection of codes, and the second stage codes the data using the identified code. The collection of codes may he noiseless codes, fixedrate quan ..."
Abstract

Cited by 44 (10 self)
 Add to MetaCart
AbstractA twostage code is a block code in which each block of data is coded in two stages: the first stage codes the identity of a block code among a collection of codes, and the second stage codes the data using the identified code. The collection of codes may he noiseless codes, fixedrate quantizers, or variablerate quantizers. We take a vector quantization approach to twostage coding, in which the first stage code can be regarded as a vector quantizer that “quantizes ” the input data of length n to one of a fixed collection of block codes. We apply the generalized Lloyd algorithm to the firststage quantizer, using induced measures of rate and distortion, to design locally optimal twostage, codes. On a source of medical images, twostage variahlerate vector quantizers designed in this way outperform standard (onestage) fixedrate vector quantizers by over 9 dB. The tail of the operational distortionrate function of the firststage quantizer determines the optimal rate of convergence of the redundancy of a universal sequence of twostage codes. We show that there exist twostage universal noiseless codes, fixedrate quantizers, and variablerate quantizers whose perletter rate and distortion redundancies converge to zero as (k/2)n ’ logn, when the universe of sources has finite dimension k. This extends the achievability part of Rissanen’s theorem from universal noiseless codes to universal quantizers. Further, we show that the redundancies converge as O(n’) when the universe of sources is countable, and as O(r~l+‘) when the universe of sources is infinitedimensional, under appropriate conditions. Index TermsTwostage, adaptive, compression, minimum description length, clustering. I.
Mutual Information, Metric Entropy, and Cumulative Relative Entropy Risk
 Annals of Statistics
, 1996
"... Assume fP ` : ` 2 \Thetag is a set of probability distributions with a common dominating measure on a complete separable metric space Y . A state ` 2 \Theta is chosen by Nature. A statistician gets n independent observations Y 1 ; : : : ; Y n from Y distributed according to P ` . For each time ..."
Abstract

Cited by 40 (2 self)
 Add to MetaCart
Assume fP ` : ` 2 \Thetag is a set of probability distributions with a common dominating measure on a complete separable metric space Y . A state ` 2 \Theta is chosen by Nature. A statistician gets n independent observations Y 1 ; : : : ; Y n from Y distributed according to P ` . For each time t between 1 and n, based on the observations Y 1 ; : : : ; Y t\Gamma1 , the statistician produces an estimated distribution P t for P ` , and suffers a loss L(P ` ; P t ). The cumulative risk for the statistician is the average total loss up to time n. Of special interest in information theory, data compression, mathematical finance, computational learning theory and statistical mechanics is the special case when the loss L(P ` ; P t ) is the relative entropy between the true distribution P ` and the estimated distribution P t . Here the cumulative Bayes risk from time 1 to n is the mutual information between the random parameter \Theta and the observations Y 1 ; : : : ;...