Add to ORKGThe paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called MiniClustering (MC), extends the partition-based approximation offered by mini-bucket elimination, to tree decompositions
The paper extends several variable elimination schemes into a two-phase message passing algorithm al...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
We analyze a family of probability distribu-tions that are characterized by an embedded combinatoria...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, cal...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on...
Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bound...
Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bound...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
This paper describes a general scheme for accomodating different types of conditional distributions ...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We develop a tree-based reparameterization framework that pro-vides a new conceptual view of a large...
The paper extends several variable elimination schemes into a two-phase message passing algorithm al...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
We analyze a family of probability distribu-tions that are characterized by an embedded combinatoria...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, cal...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on...
Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bound...
Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bound...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
This paper describes a general scheme for accomodating different types of conditional distributions ...
We propose a novel framework for approximations to intractable probabilistic models which is based o...
We analyze a family of probability distributions that are characterized by an embedded combinatorial...
We develop a tree-based reparameterization framework that pro-vides a new conceptual view of a large...
The paper extends several variable elimination schemes into a two-phase message passing algorithm al...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
We analyze a family of probability distribu-tions that are characterized by an embedded combinatoria...