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Motivation: undirected graphical models
"... Motivation: undirected graphical models (MRFs) • Powerful way to represent relationships across variables • Many applications including: computer vision, social network analysis, deep belief networks, protein folding... • In this talk, mostly focus on binary pairwise (Boolean binary or Ising) models ..."
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Motivation: undirected graphical models (MRFs) • Powerful way to represent relationships across variables • Many applications including: computer vision, social network analysis, deep belief networks, protein folding... • In this talk, mostly focus on binary pairwise (Boolean binary or Ising
Undirected Graphical Models
, 2006
"... This note proposes a novel approach for assessing the balancing hypothesis that underlies propensity score matching methods based on using an informationtheoretic approach to fit undirected graphical models. Graphical models are parametric statistical models for multivariate random variables whose ..."
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This note proposes a novel approach for assessing the balancing hypothesis that underlies propensity score matching methods based on using an informationtheoretic approach to fit undirected graphical models. Graphical models are parametric statistical models for multivariate random variables whose
Undirected Graphical Models:
, 2003
"... th the corresponding separator set, C 1 2 . (But notice that these labels might not uniquely specify an edge.) A junction tree for a graph G is a clique tree for G that satisfies the following condition. For any cliques C 1 and C 2 in the tree, every clique on the path connecting C 1 and C 2 con ..."
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th the corresponding separator set, C 1 2 . (But notice that these labels might not uniquely specify an edge.) A junction tree for a graph G is a clique tree for G that satisfies the following condition. For any cliques C 1 and C 2 in the tree, every clique on the path connecting C 1 and C 2 contains C 1 C 2 . 1 A vertex is simplicial in a graph if its neighbors form a complete subgraph. A graph is recursively simplicial if it contains a simplicial vertex v and when v is removed the subgraph that remains is recursively simplicial. Equivalence Theorem Theorem 1. The following properties of G are equivalent. 1. G is chordal. 2. G is decomposable. 3. G is recursively simplicial. 4. G has a junction tree. We shall present the proof as a series of lemmas (1 =# 2 =# 3 =# 4 =# 1). Lemma 1. G is chordal implies G is decomposable. Proof. We prove by induction that every chordal graph with n vertices is decomposable. This is trivially true for n = 1. If it is true for any n, th
Variations on undirected graphical models and their relationships
 Journal of Machine Learning Research
"... We consider undirected graphical models for discrete, finite variables. Lauritzen (1996) provides alternative definitions of such models and describes their relationships. We extend his analysis by considering another definition describing conditionally specified distributions. We describe the relat ..."
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Cited by 1 (0 self)
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We consider undirected graphical models for discrete, finite variables. Lauritzen (1996) provides alternative definitions of such models and describes their relationships. We extend his analysis by considering another definition describing conditionally specified distributions. We describe
AJunctionTreeFrameworkforUndirectedGraphicalModelSelection
, 1304
"... An undirected graphical model is a joint probability distribution defined on an undirected graph G ∗, where the vertices in the graph index a collection of random variables and the edges encode conditional independence relationships amongst random variables. The undirected graphical model selection ..."
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An undirected graphical model is a joint probability distribution defined on an undirected graph G ∗, where the vertices in the graph index a collection of random variables and the edges encode conditional independence relationships amongst random variables. The undirected graphical model selection
Undirected Graphical Models: Chordal Graphs, Decomposable Graphs, Junction Trees, and Factorizations
, 2003
"... These notes present some properties of chordal graphs, a set of undirected graphs that are important for undirected graphical models. ..."
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These notes present some properties of chordal graphs, a set of undirected graphs that are important for undirected graphical models.
Model Selection in Undirected Graphical Models with the Elastic Net
, 2010
"... Structure learning in random fields has attracted considerable attention due to its difficulty and importance in areas such as remote sensing, computational biology, natural language processing, protein networks, and social network analysis. We consider the problem of estimating the probabilistic ..."
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bilistic graph structure associated with a Gaussian Markov Random Field (GMRF), the Ising model and the Potts model, by extending previous work on l1 regularized neighborhood estimation to include the elastic net l1 + l2 penalty. Additionally, we show numerical evidence that the edge density plays a role
Distributed map inference for undirected graphical models
 In Neural Information Processing Systems (NIPS), Workshop on Learning on Cores, Clusters and Clouds
, 2010
"... Graphical models have widespread uses in information extraction and natural language processing. Recent improvements in approximate inference techniques [1, 2, 3, 4] have allowed exploration of dense models over a large number of variables. These applications include coreference resolution [5, 6], r ..."
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Cited by 4 (3 self)
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Graphical models have widespread uses in information extraction and natural language processing. Recent improvements in approximate inference techniques [1, 2, 3, 4] have allowed exploration of dense models over a large number of variables. These applications include coreference resolution [5, 6
Towards an Integer Approximation of Undirected Graphical Models
"... Data analytics for streaming sensor data brings challenges for the resource efficiency of algorithms in terms of execution time and the energy consumption simultaneously. Fortunately, optimizations which reduce the number of CPU cycles also reduce energy consumption. When reviewing the specification ..."
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consumption. If, for instance, a mobile medical device or smartphone can build a probabilistic model of the usage behavior of its user, energy models can be made more accurate and power management can be more efficient. The biggest hurdle in doing this, are the heavily restricted computational capabilities
EDML for Learning Parameters in Directed and Undirected Graphical Models
"... EDML is a recently proposed algorithm for learning parameters in Bayesian networks. It was originally derived in terms of approximate inference on a metanetwork which underlies the Bayesian approach to parameter estimation. While this initial derivation helped discover EDML in the first place an ..."
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. The new perspective has several advantages. First, it makes immediate some results that were nontrivial to prove initially. Second, it facilitates the design of EDML algorithms for new graphical models, leading to a new algorithm for learning parameters in Markov networks. We derive this algo
Results 1  10
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