Add to ORKGThis paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori hypothesis. We identify regions of completeness and provide preliminary empirical evaluation on randomly generated networks
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the fir...
In the following article we consider approximate Bayesian computation (ABC) for certain classes of t...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, cal...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, ca...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
In this paper we propose several approximation algorithms for the problems of full and partial abduc...
We present a unifying framework for exact and approximate inference in Bayesian networks. This frame...
Bayesian networks provide a useful mechanism for encoding and reasoning about uncertainty. Recent pr...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
The paper describes a branch and bound scheme that uses heuristics generated mechanically by the min...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the fir...
In the following article we consider approximate Bayesian computation (ABC) for certain classes of t...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, cal...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, ca...
In the first article we present a network based algorithm for probabilistic inference in an undirect...
In this paper we propose several approximation algorithms for the problems of full and partial abduc...
We present a unifying framework for exact and approximate inference in Bayesian networks. This frame...
Bayesian networks provide a useful mechanism for encoding and reasoning about uncertainty. Recent pr...
The research reported in this thesis focuses on approximation techniques for inference in graphical ...
The paper describes a branch and bound scheme that uses heuristics generated mechanically by the min...
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubi...
We show how to find a small loop cutset in a Bayesian network. Finding such a loop cutset is the fir...
In the following article we consider approximate Bayesian computation (ABC) for certain classes of t...