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Visar resultat 1 - 5 av 85 avhandlingar som matchar ovanstående sökkriterier.
1. Gated Bayesian Networks
Sammanfattning : 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. LÄS MER
2. Bayesian structure learning in graphical models
Sammanfattning : 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. LÄS MER
3. Essays on Bayesian Inference for Social Networks
Sammanfattning : 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. LÄS MER
4. Bayesian Models for Spatiotemporal Data from Transportation Networks
Sammanfattning : 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. LÄS MER
5. Bayesian networks: exact inference and applications in forensic statistics
Sammanfattning : 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. LÄS MER