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STATISTICAL VALIDATION OF SIMULATION MODELS:
"... Citation for published version (APA): Kleijnen, J. P. C. (1995). Statistical validation of simulation models: A case study. (CentER Discussion Paper; ..."
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Citation for published version (APA): Kleijnen, J. P. C. (1995). Statistical validation of simulation models: A case study. (CentER Discussion Paper;
normalization and statistical validation
, 2003
"... DNA microarray technology is a highthroughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, comp ..."
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, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
, 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
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Cited by 713 (8 self)
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This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I
Statistical Validation for Uncertainty Models
 Lecture Notes in Control and Information Sciences
, 1994
"... Statistical model validation is treated for a class of parametric uncertainty models and also for a more general class of nonparametric uncertainty models. We show that, in many cases of interest, this problem reduces to computing relative weighted volumes of convex sets in R N (where N is the num ..."
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Cited by 2 (0 self)
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Statistical model validation is treated for a class of parametric uncertainty models and also for a more general class of nonparametric uncertainty models. We show that, in many cases of interest, this problem reduces to computing relative weighted volumes of convex sets in R N (where N
ModelBased Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application
, 2001
"... Background: A modelbased analysis of oligonucleotide expression arrays we developed previously uses a probesensitivity index to capture the response characteristic of a specific probe pair and calculates modelbased expression indexes (MBEI). MBEI has standard error attached to it as a measure of ..."
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Cited by 751 (28 self)
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better ranking statistic for filtering genes. We can assign reliability indexes for genes in a specific cluster of interest in hierarchical clustering by resampling clustering trees. A software dChip implementing many of these analysis methods is made available. Conclusions: The modelbased approach
Statistical validation of simulation models
"... Abstract: This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis a ..."
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Cited by 1 (0 self)
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Abstract: This paper investigates various statistical methodologies for validating simulation models in automotive design. Validation metrics to compare model prediction with experimental observation, when there is uncertainty in both, are developed. Two types of metrics based on Bayesian analysis
A new learning algorithm for blind signal separation

, 1996
"... A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
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Cited by 614 (80 self)
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A new online learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
Statistical Validation of Parametric Models
"... In this paper we formulate a certain statistical model validation problem where we wish to determine the probability that a certain hypothesized parametric uncertainty model is valid given an experimental inputoutput data record. We then show that, in many instances of interest, this problem reduce ..."
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In this paper we formulate a certain statistical model validation problem where we wish to determine the probability that a certain hypothesized parametric uncertainty model is valid given an experimental inputoutput data record. We then show that, in many instances of interest, this problem
Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
 IEEE Transactions on Information Theory
, 2005
"... Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
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Cited by 574 (13 self)
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Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems
A review of image denoising algorithms, with a new one
 SIMUL
, 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
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Cited by 500 (6 self)
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The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding
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