Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 13 of 13 for “"Gaussian graphical model"”.
-
Joint Gaussian Graphical Model for multi-class and multi-level data
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks …
-
Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
… tools. The third part considers the star-shape Gaussian graphical model, which is a special case of undirected Gaussian graphical models. It is a multivariate normal distribution where the variables are grouped into one "global" group of variable set and several "local" groups of variable set. …
-
High-Dimensional Covariate-Dependent Gaussian Graphical Models
… dissertation, we propose a covariate-dependent Gaussian graphical model (cdexGGM) for capturing network structure that varies with covariates through a novel parameterization. Utilizing a likelihood framework, our methodology jointly estimates all edge and vertex parameters. We further develop …
-
Bayesian Multilevel-multiclass Graphical Model
Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian graphical models at multilevel from unknown classes. Another one is to select …
-
Provable Algorithms for Learning and Variational Inference in Undirected Graphical Models
Graphical models are a general-purpose tool for modeling complex distributions in a way which facilitates probabilistic reasoning, with numerous applications across machine learning and the sciences. This thesis deals with algorithmic and statistical problems of learning a high-dimensional …
-
Statistical Methods for Genetic Pathway-Based Data Analysis
… data. One is to propose a semi- parametric model for identifying pathways related to the zero inflated clinical outcomes; the other is to propose a multilevel Gaussian graphical model for exploring both pathway and gene level network structures. For the first problem, we develop a …
-
Bayesian Adjustment for Multiplicity
… tests for variable inclusion in linear regresson models, and tests for conditional independence in jointly normal vectors. Multiplicity adjustment in these three areas will be seen to have many common structural features. Though the modeling approach throughout is Bayesian, frequentist reasoning …
-
The Nature of Night Eating Syndrome: Using Network Analysis to Understand Causal Symptomological Relationships
… are grounded in a latent variable, medical model nosology and theory of etiology. A recently developed advanced statistical technique, network analysis, allows for better understanding of the functional relationship among symptoms of behavioral health disorders. Network analysis has been …
-
Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii
Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit into a cell. We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso …
-
Convex optimization methods for graphs and statistical modeling
… among a large number of interacting entities. Models based on graphs form one major thrust in this thesis, as graphs often provide a concise representation of the interactions among a large set of variables. A second major emphasis of this thesis are classes of structured models that satisfy …
-
Three Essays on High-Frequency and High-Dimensional Financial Data Analysis
… with Professor Hao Wang, we induce sparsity via graphical models in order to produce stable and robust covariance matrix estimates. We propose a new algorithm for Bayesian model determination in Gaussian graphical models under G-Wishart prior distributions. We first review recent developments in …
-
Knowledge-fused Identification of Condition-specific Rewiring of Dependencies in Biological Networks
Gene network modeling is one of the major goals of systems biology research. Gene network modeling targets the middle layer of active biological systems that orchestrate the activities of genes and proteins. Gene network modeling can provide critical information to bridge the gap between causes and …
-
Semiparametric and Nonparametric Methods for Complex Data
… the third is to propose a multi-class dynamic model for recognizing a pattern in the time-trend analysis. For the first topic, we propose a semiparametric omnibus test for testing the significance of a functional association between the clustered binary outcomes and covariates with measurement …