Abstract In Bayesian rule an unknown parameter is thought to be a quantity whose variation can characterized by prior distribution. Then some data are observed from population distribution function indexed the and then updated according data. The named as posterior Based on uncertainty theory, this paper first makes connection between likelihood function, proposes new method obtain with given S...

Bayesian networks are not easy to design and maintain. It is a time consuming process to update a Bayesian network even though only a small set of nodes with many occurrences has to be changed. In this paper, we describe a solution to these diiculties by taking an object oriented approach to constructing Bayesian networks by merging fragments of Bayesian networks. Our approach consists of a new...

IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software environment for creation and evaluation of belief networks and influence diagrams. IDEAL is primarily a research tool and provides an implementation of many of the latest developments in belief network and influence diagram evaluation in a unified framework. This paper describes IDEAL and some lessons learned during its dev...

During broadcast events such as the Superbowl, the U.S. Presidential and Primary debates, etc., Twitter has become the de facto platform for crowds to share perspectives and commentaries about them. Given an event and an associated large-scale collection of tweets, there are two fundamental research problems that have been receiving increasing attention in recent years. One is to extract the to...

Over the last decade, the Bayesian approach has increased in popularity in many application areas. It uses a probabilistic framework which encodes our beliefs or actions in situations of uncertainty. Information from several models can also be combined based on the Bayesian framework to achieve better inference and to better account for modeling uncertainty. The approach we adopted here is to u...