Sentence examples for graphical structure from inspiring English sources
Structural learning can be achieved by choosing the graphical structure with maximal likelihood, or its penalised versions, such as AIC and BIC.
Stochastic inference in graphical models may lead to two different types of learning: structural learning, which leads to the estimation of the graphical structure G that best describes the data and quantitative learning, that aims at estimating the parameters of a graphical model, for a given graph.
Here we introduce a new mixed graphical structure called the chain event graph that is a function of this event tree and a set of elicited equivalence relationships.
Thanks to decomposability, utility vectors attached to solutions can be compiled into a graphical structure closely related to junction trees, the so-called GAI network.
While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle.
A Q-matrix – an incidence matrix whose rows represent observable outcomes from assessment tasks and whose columns represent competency variables – provides the graphical structure of the evidence models.
The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data.
Our model is a multi-layer composite of tree-structured pictorial-structure models, each modeling human pose at a different scale and with a different graphical structure.
We also consider real-world datasets from several domains, aiming to show that multiple graphical structures underlying data can be identified and are able to provide additional problem insight.
Because chemical compounds are often represented by the graph of their covalent bonds, machine learning methods in this domain must be capable of processing graphical structures with variable size.
In this paper, we further investigate the family of asymmetric hidden Markov models (HMM-As), which generalize the emission distributions to arbitrary Bayesian-network distributions, allowing for state-specific graphical structures in the feature space.
