Learning with Graphical Models

By Wray L. Buntine. 1994

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Citations in the corpus

Explaining nonlinear classification decisions with deep Taylor decomposition (2015, methodology): “They include, for example, graphical models [1], kernels [2, 3], Gaussian processes [4], neu-”
A theory of causal learning in children: causal maps and Bayes nets. (2004, neutral): “, Jordan, 1998). The problem also has qualitative versions: for example, to predict whether the unconditional probability distribution of X is or is not equal to the conditional distribution of X given values for S—that is, whether S provides information about X—or to predict for any particular valu”
Model based Bayesian Exploration (1999, methodology): “We show that this is possible by adopting results from Bayesian learning of probabilistic models, such as Bayesian networks (Heckerman 1998).”
Constructing free-energy approximations and generalized belief propagation algorithms (2005, background): “INTRODUCTION Problems involving probabilistic inference using graphical models are important in a wide variety of disciplines, including statistical physics, signal processing, artificial intelligence, and digital communications [1], [2].”
Machine Learning and Radiology (2012, background): “Graph models (Jordan, 1998; Jordan et al., 1999) are perhaps the most popular probabilistic models in which nodes represent random variables and links between nodes denote the conditional independence structure between random variables.”
Graph Signal Processing: Overview, Challenges, and Applications (2017, background): “3) Graphical models: The focus in this area is on inference and learning from large datasets, [26], [27], [28], [29], [30].”
Ant colony optimization theory: A survey (2005, background): “[55], or graphical models and Bayesian networks [42], is relatively unexplored .”
Nonnegative Decomposition of Multivariate Information (2010, background): “The two main attempts to generalize information theory to multivariate interactions are the total correlation proposed by Watanabe [6] (also known as the multivariate constraint [7], multiinformation [8], and integration [9]) and the interaction information of McGill [10] (also known as multiple mut”
Inferring Cellular Networks Using Probabilistic Graphical Models (2004, methodology): “In this review, I examine the use of a class of mathematical models known as probabilistic graphical models (5, 6) for model-based analysis of cellular networks.”