This paper describes an abstract framework called valuation network for computation of marginals using local computation. In valuation networks, we represent knowledge using functions called valuations. Making inferences involves using two operators called marginalisation and combination. Marginalisation tells us how to coarsen a valuation by eliminating some variables. Combination tells us how to combine valuations. Making inferences from a valuation network can be simply described as finding the marginal of the joint valuation for each variable of interest. The joint valuation is the combination of all valuations. We state some simple axioms that marginalisation and combination need to satisfy to enable us to computer marginals using loca...
The goal of this contribution is to discuss local computation in credal networks — graphical models ...
In recent years, the computation of marginals out of a factorization of val-uations has been studied...
This paper proposes a new method for solving Bayesian decision problems. The method con-sists of rep...
This paper describes an abstract framework, called valuation network (VN), for representing and solv...
AbstractThe main aim of this paper is to describe two modifications to the Shenoy-Shafer architectur...
The main aim of this paper is to describe two modifications to the Shenoy–Shafer architecture with t...
Many different formalisms for treating uncertainty or, more generally, information and knowledge, ha...
AbstractMany problems of artificial intelligence, or more generally, many problems of information pr...
This paper describes an abstract framework, called valuation networks (VN), for representing and so...
The main aim of this paper is to describe two modifications to the Shenoy–Shafer architecture with t...
AbstractWe describe a data structure called binary join trees that is useful in computing multiple m...
Local computation in join trees or acyclic hypertrees has been shown to be linked to a particular al...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
AbstractLocal computation in join trees or acyclic hypertrees has been shown to be linked to a parti...
We investigate algorithms for different steps in the decision making process, focusing on systems wh...
The goal of this contribution is to discuss local computation in credal networks — graphical models ...
In recent years, the computation of marginals out of a factorization of val-uations has been studied...
This paper proposes a new method for solving Bayesian decision problems. The method con-sists of rep...
This paper describes an abstract framework, called valuation network (VN), for representing and solv...
AbstractThe main aim of this paper is to describe two modifications to the Shenoy-Shafer architectur...
The main aim of this paper is to describe two modifications to the Shenoy–Shafer architecture with t...
Many different formalisms for treating uncertainty or, more generally, information and knowledge, ha...
AbstractMany problems of artificial intelligence, or more generally, many problems of information pr...
This paper describes an abstract framework, called valuation networks (VN), for representing and so...
The main aim of this paper is to describe two modifications to the Shenoy–Shafer architecture with t...
AbstractWe describe a data structure called binary join trees that is useful in computing multiple m...
Local computation in join trees or acyclic hypertrees has been shown to be linked to a particular al...
This article was reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, 1990, ...
AbstractLocal computation in join trees or acyclic hypertrees has been shown to be linked to a parti...
We investigate algorithms for different steps in the decision making process, focusing on systems wh...
The goal of this contribution is to discuss local computation in credal networks — graphical models ...
In recent years, the computation of marginals out of a factorization of val-uations has been studied...
This paper proposes a new method for solving Bayesian decision problems. The method con-sists of rep...