Search for dissertations about: "Bayesian networks"
Showing result 1 - 5 of 84 swedish dissertations containing the words Bayesian networks.
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1. Gated Bayesian Networks
Abstract : Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. READ MORE
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2. Bayesian structure learning in graphical models
Abstract : This thesis consists of two papers studying structure learning in probabilistic graphical models for both undirected graphs anddirected acyclic graphs (DAGs).Paper A, presents a novel family of graph theoretical algorithms, called the junction tree expanders, that incrementally construct junction trees for decomposable graphs. READ MORE
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3. Essays on Bayesian Inference for Social Networks
Abstract : This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time.A social network is conceived as being a structure consisting of actors and their social interaction with each other. READ MORE
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4. Bayesian Models for Spatiotemporal Data from Transportation Networks
Abstract : Urbanization has caused a historical transformation at a global scale, and humanity is moving towards a fully connected society where cities will concentrate population, infrastructure and economic activity. A key element in the cities’ infrastructure is the transportation system, as it facilitates the mobility of people and goods. READ MORE
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5. Bayesian networks: exact inference and applications in forensic statistics
Abstract : Exact inference on Bayesian networks has been developed through sophisticated algorithms. One of which, the variable elimination algorithm, identifies smaller components of the network, called factors, on which local operations are performed. In principle this algorithm can be used on any Bayesian network. READ MORE