Results 11 to 20 of about 22,758,545 (367)
Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model [PDF]
Sensors (Basel), 2020 In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection.
H. Pham +5 more
semanticscholar +2 more sources
Frontiers in Psychology, 2019 Exploratory analyses are an important first step in psychological research, particularly in problem-based research where various variables are often included from multiple theoretical perspectives not studied together in combination before.
Nitin Bhushan +5 more
doaj +2 more sources
, 2016 Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables.
Bui, Hung +5 more
core +3 more sources
Probabilistic Graphical Model Representation in Phylogenetics [PDF]
Systematic Biology, 2013 Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development.
Boussau, Bastien +5 more
core +4 more sources
Equivalence model: A new graphical model for causal inference [PDF]
Epidemiology and Health, 2020 Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it.
Jalal Poorolajal
doaj +2 more sources
A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data [PDF]
arXiv, 2012 Gaussian graphical models are often used to infer gene networks based on microarray expression data. Many scientists, however, have begun using high-throughput sequencing technologies to measure gene expression. As the resulting high-dimensional count data consists of counts of sequencing reads for each gene, Gaussian graphical models are not optimal ...
Genevera I. Allen, Zhandong Liu
arxiv +3 more sources
A probabilistic graphical model foundation for enabling predictive digital twins at scale [PDF]
Nature Computational Science, 2020 A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale.
Michael G. Kapteyn +2 more
semanticscholar +1 more source
Statistical Science, 2004 Statistical applications in fields such as bioinformatics, information retrieval, speech processing, image processing and communications often involve large-scale models in which thousands or millions of random variables are linked in complex ways. Graphical models provide a general methodology for approaching these problems, and indeed many of the ...
Michael I. Jordan
openalex +3 more sources
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data [PDF]
Multivariate Behavioral Research, 2016 We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool.
S. Epskamp +3 more
semanticscholar +1 more source
A Tighter Bound for Graphical Models [PDF]
Neural Computation, 2001 We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field.
M.A.R. Leisink, Hilbert J. Kappen
openalex +7 more sources