Contemporary systems are comprised of massive numbers of interconnected components that interact according to a hierarchy of complex, dynamic, and unknown topologies. The unknown and varying nature of these systems necessitates the development of efficient inference methods for these STGM. A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE), however, these theoretically powerful MLE-based methods suffer from fundamental drawbacks rendering them impractical in realistic settings.
With the goal of bridging this knowledge gap, this project aims to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models, and precisely pinpoint and remedy the scenarios where it breaks down. If successful, this project will be the first systematic inference framework that can achieve the best of both worlds, computational efficiency and favorable statistical performance, in a unified fashion.
Scalable Inference of Spatially-varying Graphical Models with Applications in Genomics
Contemporary systems are comprised of massive numbers of interconnected components that interact according to a hierarchy of complex, dynamic, and unknown topologies. The unknown and varying nature of these systems necessitates the development of efficient inference methods for these STGM. A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE), however, these theoretically powerful MLE-based methods suffer from fundamental drawbacks rendering them impractical in realistic settings.
With the goal of bridging this knowledge gap, this project aims to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models, and precisely pinpoint and remedy the scenarios where it breaks down. If successful, this project will be the first systematic inference framework that can achieve the best of both worlds, computational efficiency and favorable statistical performance, in a unified fashion.