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Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations. [PDF]
Imaging Neurosci (Camb)Chandra NK +3 more
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A Spatiotemporal Probabilistic Graphical Model Based on Adaptive Expectation-Maximization Attention for Individual Trajectory Reconstruction Considering Incomplete Observations. [PDF]
Entropy (Basel)Sun X +7 more
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Prediction and Prevention of Pandemics via Graphical Model Inference and ConvexProgramming
, 2021 Krechetov M +4 more
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Scandinavian Journal of Statistics, 2003
AbstractA class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space.
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AbstractA class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space.
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1996
Abstract The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables.
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Abstract The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables.
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1995
Abstract The aim of this chapter is to provide a non-technical overview of graphical modelling. Independence graphs with both lines (undirected edges) and arrows (directed edges) are described, together with associated models in duding both discrete and continuous variables. Some discussion of causal inference is also given.
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Abstract The aim of this chapter is to provide a non-technical overview of graphical modelling. Independence graphs with both lines (undirected edges) and arrows (directed edges) are described, together with associated models in duding both discrete and continuous variables. Some discussion of causal inference is also given.
openaire +1 more source