Simplifying Explanations in Bayesian Belief Networks
نویسندگان
چکیده
Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable conngurations given an observed evidence. These conngurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we study how to simplify the explanations in such a way that the resulting conngurations are still accounting for the observed facts.
منابع مشابه
A Surface Water Evaporation Estimation Model Using Bayesian Belief Networks with an Application to the Persian Gulf
Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...
متن کاملA Surface Water Evaporation Estimation Model Using Bayesian Belief Networks with an Application to the Persian Gulf
Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...
متن کاملQualitative Verbal Explanations in Bayesian Belief Networks
Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in sy...
متن کاملProject Portfolio Risk Response Selection Using Bayesian Belief Networks
Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remai...
متن کاملExtending Recurrence Local Computation Approach Towards Ordering Composite Beliefs in Multiply Connected Bayesian Belief Networks
The Recurrence Local Computation Method (RLCM) for nding the most probable explanations (MPE) in a Bayesian belief network is valuable in assisting human beings to explain the possible causes of a set of evidences. However, RLCM works only on singly connected belief networks. This paper presents an extension of the RLCM which can be applied to multiply connected belief networks for nding arbitr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
دوره 9 شماره
صفحات -
تاریخ انتشار 2001