Probabilistic Inference in Influence Diagrams
نویسنده
چکیده
This paper is about reducing influence dia gram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to read ily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduc tion methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN in ference problems induced by the mew method are much easier to solve than those induced by the two previous methods.
منابع مشابه
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991). It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship between potential and conditional influence diagr...
متن کاملA Practical Inference Engine for Risk Assessment of Power Systems based on Hybrid Fuzzy Influence Diagrams
Risk became the crucial decision making criteria in evaluation of some control actions in power systems, but very often, these decisions are made in a highly uncertain environment. In this paper, a new graphical tool for risk assessment and decision making under uncertainty – hybrid influence diagram with fuzzy probability values and fuzzy random variables is proposed. Influence diagram is a ge...
متن کاملDecision analysis with influence diagrams using Elvira's explanation facilities
Explanation of reasoning in expert systems is necessary for debugging the knowledge base, for facilitating their acceptance by human users, and for using them as tutoring systems. Influence diagrams have proved to be effective tools for building decision-support systems, but explanation of their reasoning is difficult, because inference in probabilistic graphical models seems to have little rel...
متن کاملIntelligent Probabilistic Inference
The analysis of practical probabilistic models on the computer demands a convenient representation for the available knowledge and an efficient algorithm to perform inference. An appealing representation is the influence diagram, a network that makes explicit the random variables in a model and their probabilistic dependencies. Recent advances have developed solution procedures based on the inf...
متن کاملIntegrating Logical and Probabilistic Reasoning for Decision Making
We describe a representation and a set of inference methods that combine logic programming techniques with probabilistic network representations for uncertainty (influence diagrams). The techniques emphasize the dynamic construction and solution of probabilistic and decision-theoretic models for complex and uncertain domains. Given a query, a logical proof is produced if possible; if not, an in...
متن کامل