Stochastic optimization methods for structure learning in Gaussian graphical models and the general log-determinant optimization
Graphical models compactly represent the most significant interactions of multivariate probability distributions, provide an efficient inference framework to answer challenging statistical queries, and incorporate both expert knowledge with data to extract information from complex systems. When the...
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| Formato: | Thesis-Doctor of Philosophy |
| Idioma: | English |
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Nanyang Technological University
2020
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| Acesso em linha: | https://hdl.handle.net/10356/142033 |