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Article
Open AccessSemi-automated Rasch analysis with differential item functioning
Rasch analysis is a procedure to develop and validate instruments that aim to measure a person’s traits. However, manual Rasch analysis is a complex and time-consuming task, even more so when the possibility o...
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Article
Open AccessA novel Bayesian approach for latent variable modeling from mixed data with missing values
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values. We propose a novel Bayesian Gaussian copula factor (BGCF) approach that is...
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Article
Open AccessLearning causal structure from mixed data with missing values using Gaussian copula models
We consider the problem of causal structure learning from data with missing values, assumed to be drawn from a Gaussian copula model. First, we extend the ‘Rank PC’ algorithm, designed for Gaussian copula mode...
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Article
Open AccessA scalable preference model for autonomous decision-making
Emerging domains such as smart electric grids require decisions to be made autonomously, based on the observed behaviors of large numbers of connected consumers. Existing approaches either lack the flexibility...
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Article
Open AccessA Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)
Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are often comorbid. The purpose of this study is to explore the relationships between ASD and ADHD symptoms by applying causal...
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Article
Open AccessHandling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD
Causal discovery is an increasingly important method for data analysis in the field of medical research. In this paper, we consider two challenges in causal discovery that occur very often when working with me...
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Chapter and Conference Paper
Copula PC Algorithm for Causal Discovery from Mixed Data
We propose the ‘Copula PC’ algorithm for causal discovery from a combination of continuous and discrete data, assumed to be drawn from a Gaussian copula model. It is based on a two-step approach. The first ste...
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Chapter and Conference Paper
Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data
Causal discovery is an increasingly popular method for data analysis in the field of medical research. In this paper we consider two challenges in causal discovery that occur very often when working with medic...
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Chapter and Conference Paper
Causal Discovery from Databases with Discrete and Continuous Variables
Bayesian Constraint-based Causal Discovery (BCCD) is a state-of-the-art method for robust causal discovery in the presence of latent variables. It combines probabilistic estimation of Bayesian networks over su...
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Article
Open AccessEfficiently learning the preferences of people
This paper presents a framework for optimizing the preference learning process. In many real-world applications in which preference learning is involved the available training data is scarce and obtaining labe...
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Chapter
Adaptive Control Strategies for Productive Toner Printers
This chapter discusses design considerations for industrial systems and processes when embedded systems allow to intelligently influence the system in real-time. It is shown that in such embedded systems the c...
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Chapter and Conference Paper
Learning from Multiple Annotators with Gaussian Processes
In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, possibly noisy, labels from multiple anno...
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Chapter and Conference Paper
Combining Task Execution and Background Knowledge for the Verification of Medical Guidelines
The use of a medical guideline can be seen as the execution of computational tasks, sequentially or in parallel, in the face of patient data. It has been shown that many of such guidelines can be represented a...
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Chapter and Conference Paper
The Role of Model Checking in Critiquing Based on Clinical Guidelines
Medical critiquing systems criticise clinical actions performed by a physician. In order to provide useful feedback, an important task is to find differences between the actual actions and a set of ‘ideal’ act...
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Article
A quantitative analysis of the robustness of knowledge-based systems through degradation studies
The overall aim of this paper is to provide a general setting for quantitative quality measures of knowledge-based system behaviour that is widely applicable to many knowledge-based systems. We propose a gener...
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Chapter and Conference Paper
Scalable Instance Retrieval for the Semantic Web by Approximation
Approximation has been identified as a potential way of reducing the complexity of logical reasoning. Here we explore approximation for speeding up instance retrieval in a Semantic Web context. For OWL ontolog...
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Chapter and Conference Paper
Approximating Description Logic Classification for Semantic Web Reasoning
In many application scenarios, the use of the Web ontology language OWL is hampered by the complexity of the underlying logic that makes reasoning in OWL intractable in the worst case. In this paper, we addres...
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Chapter and Conference Paper
Torture Tests: A Quantitative Analysis for the Robustness of Knowledge-Based Systems
The overall aim of this paper is to provide a general setting for quantitative quality measures of Knowledge-Based System behavior which is widely applicable to many Knowledge-Based Systems. We propose a gener...
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Chapter and Conference Paper
Formally Verifying Dynamic Properties of Knowledge Based Systems
In this paper we study dynamic properties of knowledge-based systems. We argue the importance of such dynamic properties for the construction and analysis of knowledge-based systems. We present a case-study of...
