6 Result(s)
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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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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...
