Add to ORKGWe consider the question of how well a given distribution can be approx-imated with probabilistic graphical models. We introduce a new param-eter, effective treewidth, that captures the degree of approximability as a tradeoff between the accuracy and the complexity of approximation. We present a simple approach to analyzing achievable tradeoffs that ex-ploits the threshold behavior of monotone graph properties, and provide experimental results that support the approach.
We present a family of approximation techniques for probabilistic graph-ical models, based on the us...
We present two techniques for constructing sample spaces that approximate 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...
Graphical models provide a convenient representation for a broad class of probability distributions....
Probabilistic graphical models offer a powerful framework to account for the dependence structure be...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Probabilistic graphical models offer a powerful framework to account for the dependence structure be...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
The problem of finding the most probable explanation to a designated set of variables given partial ...
We present a family of approximation techniques for probabilistic graphical models, based on the us...
We present a family of approximation techniques for probabilistic graphical models, based on the use...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
'A graphical models is a powerful tool to deal with complex probability models. Although in principl...
We present a family of approximation techniques for probabilistic graph-ical models, based on the us...
We present two techniques for constructing sample spaces that approximate 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...
Graphical models provide a convenient representation for a broad class of probability distributions....
Probabilistic graphical models offer a powerful framework to account for the dependence structure be...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
Probabilistic graphical models offer a powerful framework to account for the dependence structure be...
Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertai...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
The problem of finding the most probable explanation to a designated set of variables given partial ...
We present a family of approximation techniques for probabilistic graphical models, based on the us...
We present a family of approximation techniques for probabilistic graphical models, based on the use...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
'A graphical models is a powerful tool to deal with complex probability models. Although in principl...
We present a family of approximation techniques for probabilistic graph-ical models, based on the us...
We present two techniques for constructing sample spaces that approximate probability distributions....
The research reported in this thesis focuses on approximation techniques for inference in graphical ...