Thomas D. Nielsen

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Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of(More)
Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we de­ termine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over its past. How­ ever, some of the past may be irrelevant for the decision, and for computational reasons it(More)
Obtaining accurate system models for verification is a hard and time consuming process, which is seen by industry as a hindrance to adopt otherwise powerful modeldriven development techniques and tools. In this paper we pursue an alternative approach where an accurate high-level model can be automatically constructed from observations of a given black-box(More)
The naive Bayes model makes the often unrealistic assumption that the feature variables are mutually independent given the class variable. We interpret a violation of this assumption as an indication of the presence of latent variables, and we show how latent variables can be detected. Latent variable discovery is interesting, especially for medical(More)
Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm on learning probabilistic automata to reactive systems, where(More)
Abstract. Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate system models from data in the form of observations of(More)
In this paper we study the problem of exact inference in hybrid Bayesian networks using mixtures of truncated basis functions (MoTBFs). We propose a structure for handling probability potentials called Sum-Product factorized potentials, and show how these potentials facilitate efficient inference based on i) properties of the MoTBFs and ii) ideas similar to(More)
The effect of Salmonella on milk production is not well established in cattle. The objective of this study was to investigate whether introduction of Salmonella into dairy cattle herds was associated with reduced milk yield and determine the duration of any such effect. Longitudinal data from 2005 through 2009 were used, with data from 12 mo before until 18(More)
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradually being constructed as observations of the environment are(More)
In this paper we describe a new method for learning hybrid Bayesian network models from data. The method utilizes a kernel density estimator, which is in turn “translated” into a mixture of truncated basis functions-representation using a convex optimization technique. We argue that these estimators approximate the maximum likelihood estimators, and compare(More)