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Structure learning in graphical modeling
A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …
correspond to variables of interest. The edges of the graph reflect allowed conditional …
High-dimensional semiparametric Gaussian copula graphical models
H Liu, F Han, M Yuan, J Lafferty, L Wasserman - 2012 - projecteuclid.org
We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently
and robustly estimating high-dimensional undirected graphical models. To achieve …
and robustly estimating high-dimensional undirected graphical models. To achieve …
Fast community detection by SCORE
J ** - 2015 - projecteuclid.org
Supplementary material for “Fast communication detetion by SCORE”. Owing to space
constraints, the technical proofs are relegated a supplementary document. The …
constraints, the technical proofs are relegated a supplementary document. The …
A survey on latent tree models and applications
In data analysis, latent variables play a central role because they help provide powerful
insights into a wide variety of phenomena, ranging from biological to human sciences. The …
insights into a wide variety of phenomena, ranging from biological to human sciences. The …
Tiger: A tuning-insensitive approach for optimally estimating gaussian graphical models
H Liu, L Wang - 2017 - projecteuclid.org
We propose a new procedure for optimally estimating high dimensional Gaussian graphical
models. Our approach is asymptotically tuning-free and non-asymptotically tuning …
models. Our approach is asymptotically tuning-free and non-asymptotically tuning …
Rs-forest: A rapid density estimator for streaming anomaly detection
Anomaly detection in streaming data is of high interest in numerous application domains. In
this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in …
this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in …
Transformation autoregressive networks
The fundamental task of general density estimation $ p (x) $ has been of keen interest to
machine learning. In this work, we attempt to systematically characterize methods for density …
machine learning. In this work, we attempt to systematically characterize methods for density …
Adversarial random forests for density estimation and generative modeling
We propose methods for density estimation and data synthesis using a novel form of
unsupervised random forests. Inspired by generative adversarial networks, we implement a …
unsupervised random forests. Inspired by generative adversarial networks, we implement a …
[HTML][HTML] A new extension of Weibull distribution: properties and different methods of estimation
The Weibull distribution has been generalized by many authors in recent years. Here, we
introduce a new generalization of the Weibull distribution, called Alpha logarithmic …
introduce a new generalization of the Weibull distribution, called Alpha logarithmic …
Alpha-power transformed Lindley distribution: properties and associated inference with application to earthquake data
The Lindley distribution has been generalized by many authors in recent years. A new two-
parameter distribution with decreasing failure rate is introduced, called Alpha Power …
parameter distribution with decreasing failure rate is introduced, called Alpha Power …