[PDF] Top 20 Estimation of Graphical Models through Structured Norm Minimization
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Estimation of Graphical Models through Structured Norm Minimization
... SSON and consequently has very different prediction risk behavior. The same argument illustrates the advantages of the proposed SSON penalty over the well-known elastic net penalty. The elastic net is a combination of ... See full document
48
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
... trices estimation via a Gaussian mixture ...heterogeneous graphical model estimation, Saegusa and Shojaie (2016) proposed an inter- esting two-stage method which used hierarchical clustering to ... See full document
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Regularized Estimation of Piecewise Constant Gaussian Graphical Models:The Group Fused Graphical Lasso
... Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time- ...Traditionally, graphical models are estimated under the assumption that data is drawn ... See full document
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Joint Structural Estimation of Multiple Graphical Models
... multiple graphical models under different assumptions on how the models are ...all graphical models under ...across models through the the non-connected nodes, but does ... See full document
48
Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models
... multi-layered graphical models provides insight into understanding the con- ditional relationships among nodes within layers after adjusting for and quantifying the effects of nodes from other ... See full document
51
High-dimensional Covariance Estimation Based On Gaussian Graphical Models
... for estimation of high-dimensional covariance and inverse covariance matrices where the dimension p of the matrix may greatly exceed the sample size ...covariance estimation can be classified into two main ... See full document
52
Estimation in causal graphical models
... Useful assumptions that help us to define and characterise a prior distribution associated with each network structure in the equivalence class of Bayesian networks are local and global [r] ... See full document
201
Stable Graphical Models
... is structured as follows : Section ...α-SG models and establishes that these models are Bayesian networks that also represent multivariate stable distributions with discrete spectral ...α-SG ... See full document
36
Forest Density Estimation
... graph estimation and density estimation in high dimensions, using a family of density estimators based on forest structured undirected graphical ...density estimation, we do not assume ... See full document
45
Node-Based Learning of Multiple Gaussian Graphical Models
... Gaussian graphical models cor- responding to a single set of variables under several distinct ...some structured differences between ...consider estimation under two dis- tinct assumptions: ... See full document
44
Structured Ramp Loss Minimization for Machine Translation
... Every statistical MT system relies on a training al- gorithm to fit the parameters of a scoring function to examples from parallel text. Well-known examples include MERT (Och, 2003), MIRA (Chiang et al., 2008), and PRO ... See full document
11
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
... Next we provide an analysis of the performance of the weighted trace-norm in 1-bit matrix completion. Given the knowledge of the true sampling distribution, we establish an upper bound on the error in recovering M ... See full document
29
Learning Graphical Models With Hubs
... Interestingly, some of these genes have known regulatory roles. PTPN2 is known to be a signaling molecule that regulates a variety of cellular processes including cell growth, differentiation, mitotic cycle, and ... See full document
35
Optimal solution of the nearest correlation matrix problem by minimization of the maximum norm
... simply. Through the preliminary computational results, the authors demonstrated the robustness of the algorithm and showed that sparsity can be successfully ... See full document
17
An Efficient and User Privacy-Preserving Routing Protocol for Wireless Mesh Networks
... delay estimation: An important issue in a routing protocol is end-to-end delay ...observed through simulation that a RREP-based estimator overestimates while a hop-count-based estimator underestimates the ... See full document
14
Uncertainty Quantification in the Estimation of Probability Distributions on Parameters in Size-Structured Population Models
... I have also been blessed to meet some great individuals at NC State who have not only helped in my studies but who have also proven to be great friends. I would like to extend my thanks to those I have had the pleasure ... See full document
166
On the use of two L1 norm minimization methods in geodetic networks
... The least squares adjustment is a standard and powerful method to estimate the unique and optimum solution for unknown parameters. It should be assumed, for better statistical interpretation of the results, that the ... See full document
8
Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points
... Zhou et al. (2009); Kolar et al. (2010); Roy et al. (2017), we focus on settings where the change occurring at a given change-point is global in the sense that it affects the joint distribution of all nodes. This differs ... See full document
38
Tonal signal detection in passive sonar systems using atomic norm minimization
... The primary goal of this paper is to put forth a new approach to estimate the frequency components of the tonal signal. The key idea of the proposed approach is to formulate the tonal frequency estimation problem ... See full document
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