Results 11 to 20 of about 139,233 (252)
Solving linear-quadratic conditional Gaussian influence diagrams
International Journal of Approximate Reasoning, 2005 This paper considers the problem of solving Bayesian decision problems with a mixture of continuous and discrete variables. We focus on exact evaluation of linear-quadratic conditional Gaussian influence diagrams (LQCG influence diagrams) with additively
Madsen, A.L., Jensen, F.
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The Complexity of Approximately Solving Influence Diagrams [PDF]
CoRR, 2012 Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams.
Mauá, D. D. +2 more
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Markov Influence Diagrams [PDF]
Medical Decision Making, 2017 Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees.
FRANCISCO J Diez +2 more
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Probabilistic Inference in Influence Diagrams [PDF]
Computational Intelligence, 1998 This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems that are as easy to solve as possible. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs.
Nevin L. Zhang
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Enterprise architecture analysis with extended influence diagrams
Information Systems Frontiers, 2007 The discipline of enterprise architecture advocates the use of models to support decision-making on enterprise-wide information system issues. In order to provide such support, enterprise architecture models should be amenable to analyses of various ...
Pontus Johnson +2 more
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Influence Diagrams—Historical and Personal Perspectives [PDF]
Decision Analysis, 2005 The usefulness of graphical models in reasoning and decision making stems from facilitating four main computational features: (1) modular representation of probabilities, (2) systematic construction methods, (3) explicit encoding of independencies, and (4) efficient inference procedures.
Pearl, Judea
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Improving User Comprehension of Euler Diagrams [PDF]
, 2013 The graphical choices made when laying out Euler diagrams impact upon both aesthetic quality and comprehensiveness. Graphical choices include the shape, size and colour of closed curves which are commonly described as retinal variables to which we are ...
Howse, John +9 more
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Developing a Decision Analytic Framework Based on Influence Diagrams in Relation to Mass Evacuations [PDF]
, 2013 Presented at International Conference on Emergency Preparedness "The Challenges of Mass Evacuation" 21st - 23rd September 2010 Aston Business SchoolIn this paper, we examine the role which decision analysis can play in a situation requiring a mass ...
McNaught, Ken R., Zagorecki, A.
core
A New Bounding Scheme for Influence Diagrams
, 2021 Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision ...
Lee, Junkyu +2 more
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Extension Model of Influence Diagrams
, 2011 An influence diagram is a kind of graphical model that can represent both the probabilistic relationship between variables and can easy to make decisions. It can make full use of Bayesian Network and Decisiton Tree.
Qing Song Peng
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