54 citations · 312 across the 15 of their papers we have counts for
16 papers
Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (1996)
Eric Horvitz, Finn Jensen
This is the Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, which was held in Portland, OR, August 1-4, 1996
Analysis in HUGIN of Data Conflict
Bo Chamberlain, Finn Verner Jensen, Frank Jensen +1
After a brief introduction to causal probabilistic networks and the HUGIN approach, the problem of conflicting data is discussed. A measure of conflict is defined, and it is used i…
aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
Kristian G. Olesen, Steffen L. Lauritzen, Finn Verner Jensen
The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is an extension of the HUGIN shell, and is based on the methods reported by Spiegelhalter and Lauritzen (19…
From Influence Diagrams to Junction Trees
Frank Jensen, Finn Verner Jensen, Soren L. Dittmer
We present an approach to the solution of decision problems formulated as influence diagrams. This approach involves a special triangulation of the underlying graph, the constructi…
Optimal Junction Trees
Finn Verner Jensen, Frank Jensen
The paper deals with optimality issues in connection with updating beliefs in networks. We address two processes: triangulation and construction of junction trees. In the first par…
Cautious Propagation in Bayesian Networks
Finn Verner Jensen
Consider the situation where some evidence e has been entered to a Bayesian network. When performing conflict analysis, sensitivity analysis, or when answering questions like "What…
MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat
Allan Leck Jensen, Finn Verner Jensen
We present a prototype of a decision support system for management of the fungal disease mildew in winter wheat. The prototype is based on an influence diagram which is used to det…
Myopic Value of Information in Influence Diagrams
Soren L. Dittmer, Finn Verner Jensen
We present a method for calculation of myopic value of information in influence diagrams (Howard & Matheson, 1981) based on the strong junction tree framework (Jensen, Jensen & Dit…
Lazy Propagation in Junction Trees
Anders L. Madsen, Finn Verner Jensen
The efficiency of algorithms using secondary structures for probabilistic inference in Bayesian networks can be improved by exploiting independence relations induced by evidence an…
Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation
Yanping Xiang, Finn Verner Jensen
As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient inference methods is an ongoing effort. Multiply sectio…
Welldefined Decision Scenarios
Thomas D. Nielsen, Finn Verner Jensen
Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved…
Lazy Evaluation of Symmetric Bayesian Decision Problems
Anders L. Madsen, Finn Verner Jensen
Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the ef…
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen +1
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks determ…
Representing and Solving Asymmetric Bayesian Decision Problems
Thomas D. Nielsen, Finn Verner Jensen
This paper deals with the representation and solution of asymmetric Bayesian decision problems. We present a formal framework, termed asymmetric influence diagrams, that is based o…
Unconstrained Influence Diagrams
Finn Verner Jensen, Marta Vomlelova
We extend the language of influence diagrams to cope with decision scenarios where the order of decisions and observations is not determined. As the ordering of decisions is depend…
The Cost of Troubleshooting Cost Clusters with Inside Information
Thorsten J. Ottosen, Finn Verner Jensen
Decision theoretical troubleshooting is about minimizing the expected cost of solving a certain problem like repairing a complicated man-made device. In this paper we consider situ…