Evaluating Influence Diagrams
نویسنده
چکیده
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/informs.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.
منابع مشابه
Possibilistic Influence Diagrams
In this article we present the framework of Possibilistic Influence Diagrams (PID), which allow to model in a compact form problems of sequential decision making under uncertainty, when only ordinal data on transitions likelihood or preferences are available. The graphical part of a PID is exactly the same as that of usual influence diagrams, however the semantics differ. Transition likelihoods...
متن کاملEvaluating Influence Diagrams
In this paper we will survey the history of Influence Diagrams from their origin in Decision Theory to modern AI uses. We will compare the various methods that have been used to find an optimal strategy for a given influence diagram. These methods have various advantages under different assumptions and there is a progression to the present of richer, more general solutions. We also look at an a...
متن کاملThree new sensitivity analysis methods for influence diagrams
Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re...
متن کاملExplaining Predictions in Bayesian Networks and Influence Diagrams
As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions and recommendations of probabilistic systems. We analyze the issues involved in defining, computing and evaluating such explanations and present an algorithm to c...
متن کاملModel-Based Influence Diagrams for Machine Vision
We show the soundness of automated con trol of machine vision systems based on in cremental creation and evaluation of a par ticular family of influence diagrams that rep resent hypotheses of imagery interpretation and possible subsequent processing decisions. In our approach, model-based machine vi sion techniques are integrated with hierarchi cal Bayesian inference to provide a framewor...
متن کاملSolving influence diagrams: Exact algorithms
Influence diagrams were developed as a graphical representation for formulating a decision analysis model, facilitating communication between the decision maker and the analyst [1]. We show several approaches for evaluating influence diagrams, determining the optimal strategy for the decision maker and the value or certain equivalent of the decision situation when that optimal strategy is appli...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Operations Research
دوره 34 شماره
صفحات -
تاریخ انتشار 1986