publication . Doctoral thesis . 2015

Structure Learning of Linear Bayesian Networks in High-Dimensions

Aragam, Nikhyl Bryon;
Open Access English
  • Published: 01 Jan 2015
  • Publisher: eScholarship, University of California
  • Country: Mexico
Abstract
Research into graphical models is a rapidly developing enterprise, garnering significant interest from both the statistics and machine learning communities. A parallel thread in both communities has been the study of low-dimensional structures in high-dimensional models where $p\gg n$. Recently, there has been a surge of interest in connecting these threads in order to understand the behaviour of graphical models in high-dimensions. Due to their relative simplicity, undirected models such as the Gaussian graphical model and Ising models have received most of the attention, whereas directed graphical models have received comparatively little attention. An importa...
Subjects
free text keywords: Statistics, Applied mathematics, Bayesian networks, Graphical modeling, High-dimensional statistics, Nonconvex optimization, Regularization, Structure learning
Related Organizations
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue