Publicaties
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Formal and informal model selection with incomplete data Universiteit Hasselt KU Leuven
Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the ...
Modularity-based model selection for kernel spectral clustering KU Leuven
A proper way of choosing the tuning parameters in a kernel model has a fundamental importance in determining the success of the model for a particular task. This paper is related to model selection in the framework of community detection on weighted and unweighted networks by means of a kernel spectral clustering model. Here we propose a new method based on Modularity (a popular measure of community structure in a network) which can deal with ...
Model selection for continuous commissioning of HVAC-systems in office buildings: a review KU Leuven
This paper presents an overview of literature and procedures about real-life, state-of-the-art implementations of model-based (MB) Continuous Commissioning (CCx) in offce buildings. The focus is on the building- and HVAC-models used for each of three distinct CCx-domains: The identifcation of energy conserving opportunities (ECOs), fault detection, diagnosis, evaluation and overhaul (FDDe) and model-based control (MBC). For each domain, the ...
The Monte Carlo validation framework for the discriminant partial least squares model extended with variable selection methods applied to authenticity studies of Viagra (R) based on chromatographic impurity profiles Universiteit Antwerpen
The aim of this work was to develop a general framework for the validation of discriminant models based on the Monte Carlo approach that is used in the context of authenticity studies based on chromatographic impurity profiles. The performance of the validation approach was applied to evaluate the usefulness of the diagnostic logic rule obtained from the partial least squares discriminant model (PLS-DA) that was built to discriminate authentic ...
Image-quality evaluation and model selection with maximum a posteriori probability Universiteit Antwerpen
The maximum a posteriori (MAP) probability rule for atom column detection can also be used as a tool to evaluate the relation between scanning transmission electron microscopy (STEM) image quality and atom detectability. In this chapter, a new image-quality measure is proposed that correlates well with atom detectability, namely the integrated contrast-to-noise ratio (ICNR). Furthermore, the working principle of the MAP probability rule is ...
Probabilistic risk model to assess the potential for resistance selection following the use of anti-microbial medicated feed in pigs Universiteit Gent
Bayesian model selection for electromagnetic kaon production in the Regge-plus-resonance framework Universiteit Gent
CHull: A generic convex hull based model selection method KU Leuven
When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in PCA/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they ...
Using information-theoretic approaches for model selection in meta-analysis KU Leuven
Meta-regression can be used to examine the association between effect size estimates and the characteristics of the studies included in a meta-analysis using regression-type methods. By searching for those characteristics (i.e., moderators) that are related to the effect sizes, we seek to identify a model that represents the best approximation to the underlying data generating mechanism. Model selection via testing, either through a series of ...