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Probabilistic Inference in Influence Diagrams [article]

Nevin Lianwen Zhang
2013 arXiv   pre-print
This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems.  ...  The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.  ...  Introduction Influence diagrams (IDs) (Howard and Matheson 1984 ) are a popular framework for decision analysis.  ... 
arXiv:1301.7416v1 fatcat:eiytgakgzzhj7fiujvwhsuttbm

Probabilistic Inference in Influence Diagrams

Nevin Lianwen Zhang
1998 Computational intelligence  
This paper studies the relationship between probabilistic inference in Bayesian networks and evaluation of in uence diagrams.  ...  We clearly identify and separate from other computations probabilistic inference subproblems that must be solved in order to evaluate an in uence diagram.  ...  This work leads to a new method for evaluating in uence diagrams where arbitrary Bayesian network inference algorithms can be used for probabilistic inference.  ... 
doi:10.1111/0824-7935.00073 fatcat:wsqesrrbrfeerg2aip36btopme

Financial Assessment of London Plan Policy 4A.2 by Probabilistic Inference and Influence Diagrams [chapter]

Amin Hosseinian-Far, Elias Pimenidis, Hamid Jahankhani, D. C. Wijeyesekera
2011 IFIP Advances in Information and Communication Technology  
This paper reflects a financial assessment by means of Influence Diagrams based upon the London Plan policies 4A.X.  ...  Furthermore, the proposals include chapters outlining adjustments in each facet. Policy 4A.2 reflects the Climate Change Mitigation scheme.  ...  The application of the plan on 260 approved developments has revealed the following results: Influence Diagrams An Influence Diagram (ID) is a network visualization for probabilistic models [8] .  ... 
doi:10.1007/978-3-642-23960-1_7 fatcat:p4ftpb2jh5btbevt3hx6dkqpbe

Integrating Logical and Probabilistic Reasoning for Decision Making [article]

John S. Breese, Edison Tse
2013 arXiv   pre-print
We describe a representation and a set of inference methods that combine logic programming techniques with probabilistic network representations for uncertainty (influence diagrams).  ...  Given a query, a logical proof is produced if possible; if not, an influence diagram based on the query and the knowledge of the decision domain is produced and subsequently solved.  ...  Probabilistic Inference Inference is initiated by a identifying an initial goal (the query), Go= (P t1 t2 .... tk), and an empty influence diagram, N.  ... 
arXiv:1304.2751v1 fatcat:ki7rh3tgunaonlqkbustd33zve

Special issue on PGM'04: Second European workshop on probabilistic graphical models 2004

Peter J.F. Lucas, José A. Gámez, Antonio Salmerón
2006 International Journal of Approximate Reasoning  
Roughly, the papers focus on either Bayesian networks or influence diagrams, and either deal with problems related to probabilistic or decision-theoretic inference or structure and parameter learning.  ...  The workshop was an instant success in 2002 when it was held for the first time in Cuenca, Spain, as it was able to attract interest from the entire research community in probabilistic graphical models  ...  Puerta, Rafael Rumí, Prakash Shenoy, Jirka Vomlel and Marta Vomlelová for their help in the review process.  ... 
doi:10.1016/j.ijar.2005.10.008 fatcat:qvtvmaxq4vbmdc7o7g6tmbu2ky

Intelligent Probabilistic Inference [article]

Ross D. Shachter
2013 arXiv   pre-print
The general probabilistic inference problem is posed in Section 3. In Section 4 the transformations on the diagram are developed and then put together into a solution procedure in Section 5.  ...  An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies.  ...  Th general probabilistic inference problem is posed in Section 3. In Section 4 the transformations on the diagram are developed and then put together into a soluti procedure in Section 5.  ... 
arXiv:1304.3446v1 fatcat:m46dnon4vzfzno73a2up7qlfl4

Decision Making Using Probabilistic Inference Methods [article]

Ross D. Shachter, Mark Alan Peot
2013 arXiv   pre-print
In addition to general approaches which need know nothing about the actual probabilistic inference method, we suggest some simple modifications to the clustering family of algorithms in order to efficiently  ...  Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental normative arguments of decision theory.  ...  ACKNOWLEDGEMENTS We benefitted greatly from the comments and suggestions of Stig Andersen, David Heckerman, Prakash Shenoy, the two anonymous referees, and a number of students in the EES department This  ... 
arXiv:1303.5428v1 fatcat:u2ptmnnm4jf3tmpcmbuyfsy2aa

Directed Reduction Algorithms and Decomposable Graphs [article]

Ross D. Shachter, Stig K. Andersen, Kim-Leng Poh
2013 arXiv   pre-print
In recent years, there have been intense research efforts to develop efficient methods for probabilistic inference in probabilistic influence diagrams or belief networks.  ...  diagram.  ...  Introduction In recent years, there have been intense research efforts to develop efficient methods for probabilistic inference in probabilistic influence diagrams or belief networks.  ... 
arXiv:1304.1110v1 fatcat:si3puxhz6vfdhpoucaagriwzdq

The Influence of Influence Diagrams on Artificial Intelligence

Craig Boutilier
2005 Decision Analysis  
s article "Influence Diagrams" has had a substantial impact on research in artificial intelligence (AI).  ...  In this perspective, I briefly discuss the importance of influence diagrams as a model for decision making under uncertainty in the AI research community; but I also identify some of the less direct, but  ...  Another reasonably direct impact of "Influence Diagrams" derives from its role in the development of graphical models for probabilistic modeling and inference.  ... 
doi:10.1287/deca.1050.0054 fatcat:4snqddg67jcs7aboxpjogcidyy

IDES: influence diagram based expert system

Alice M. Agogino, Ashutosh Rege
1987 Mathematical Modelling  
Infhrence diagrams have been used effectively in applied decision analysis to model complex ~. systems, identify probabilistic dependence and characterize the flow of information.  ...  It is proposed that an interactive computer program that automates this influence diagram technology would provide an excellent tool for building expert systems.  ...  Probabilistic Inference Probabilistic inference is equivalent to determining the probability distribution (G(X)) Y.H) where X and Y are sets of nodes given in the diagram.  ... 
doi:10.1016/0270-0255(87)90579-3 fatcat:6tigwqsnbvc2fkjhlngj7e7kle

Efficient Inference on Generalized Fault Diagrams [article]

Ross D. Shachter, Leonard Bertrand
2013 arXiv   pre-print
The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined.  ...  to the solution of mixed logical-probabilistic inference problems.  ...  In a fault influence diagram, the probability of success for a logical operator may depend on other logical operators and probabilistic events.  ... 
arXiv:1304.2758v1 fatcat:4hv44j5aejd7po2jaq7lfvvqci

From the Guest Editor…:New Contributions and Reflections on Graph-Based Representations for Decision Analysis

Eric Horvitz
2005 Decision Analysis  
reflections on the theoretical and practical influences of probabilistic graphical models in several realms.  ...  In this second volume of the special issue of Decision Analysis on graph-based representations for decision analysis, we present two articles and four brief invited perspective pieces, capturing personal  ...  Craig Boutilier reflects about the impact of influence diagrams in AI.  ... 
doi:10.1287/deca.1050.0061 fatcat:nibfo3ahfbbfvchxm7yodoliou

A Method for Using Belief Networks as Influence Diagrams [article]

Gregory F. Cooper
2013 arXiv   pre-print
More generally, knowing the relationship between belief-network and influence-diagram problems may be useful in the design and development of more efficient influence diagram algorithms.  ...  In particular, both exact and approximation belief-network algorithms may be applied to solve influence-diagram problems.  ...  This work has been supported in part by National Science Foundation grant IRI-870371 0 and by National Library of Medicine grant LM-07033.  ... 
arXiv:1304.2346v1 fatcat:rrgufdldqzgmro5haku5tqvcju

Using Potential Influence Diagrams for Probabilistic Inference and Decision Making [article]

Ross D. Shachter, Pierre Ndilikilikesha
2013 arXiv   pre-print
The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha,  ...  In particular, we show how to convert a potential influence diagram into a conditional influence diagram, and how to view the potential influence diagram operations in terms of the conditional influence  ...  INTRODUCTION The potential influence diagram ( PID) is a generalization of the standard influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha  ... 
arXiv:1303.1500v1 fatcat:fubbmcq32ffp7gqgf7oz3u74b4

Structural Controllability and Observability in Influence Diagrams [article]

Brian Y. Chan, Ross D. Shachter
2013 arXiv   pre-print
Influence diagram is a graphical representation of belief networks with uncertainty. This article studies the structural properties of a probabilistic model in an influence diagram.  ...  Controllability corresponds to the ability to control a system while observability analyzes the inferability of its variables. Both properties can be determined by the ranks of the system matrices.  ...  The investment decision is modeled with an influence diagram as seen in figure 11 .  ... 
arXiv:1303.5394v1 fatcat:3q2pcizhgzgntgtzcabqxw2w3e
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