Add to ORKGWe analyze a family of probability distributions that are characterized by an embedded combinatorial structure. This family includes models having arbitrary treewidth and arbitrary sized factors. Unlike general models with such freedom, where the “most probable explanation” (MPE) problem is inapproximable, the combinatorial structure within our model, in particular the indirect use of submodularity, leads to several MPE algorithms that all have approximation guarantees
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
We consider the question of how well a given distribution can be approximated with probabilistic gra...
We describe the Multiplicative Approximation Scheme (MAS) for approximate inference in multiplicativ...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We analyze a family of probability distribu-tions that are characterized by an embedded combinatoria...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
We consider the question of how well a given distribution can be approx-imated with probabilistic gr...
We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of e...
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper ...
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper ...
We propose a computationally feasible inference method in finite games of complete information. Gali...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the fir...
When we left off with the Joint Tree Algorithm and the Max-Sum Algorithm last class, we had crafted ...
Graphical models provide a convenient representation for a broad class of probability distributions....
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
We consider the question of how well a given distribution can be approximated with probabilistic gra...
We describe the Multiplicative Approximation Scheme (MAS) for approximate inference in multiplicativ...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We analyze a family of probability distribu-tions that are characterized by an embedded combinatoria...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
We consider the question of how well a given distribution can be approx-imated with probabilistic gr...
We introduce cooperative cut, a minimum cut problem whose cost is a submodular function on sets of e...
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper ...
It was recently proved using graph covers (Ruozzi, 2012) that the Bethe partition function is upper ...
We propose a computationally feasible inference method in finite games of complete information. Gali...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the fir...
When we left off with the Joint Tree Algorithm and the Max-Sum Algorithm last class, we had crafted ...
Graphical models provide a convenient representation for a broad class of probability distributions....
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
We consider the question of how well a given distribution can be approximated with probabilistic gra...
We describe the Multiplicative Approximation Scheme (MAS) for approximate inference in multiplicativ...