It has been widely recognized that uncertainty is an inevitable aspect of diagnosis and treatment of medical disorders. Such uncertainties hence, need to be considered in computerized medical models. The existing medical modeling techniques however, have mainly focused on capturing uncertainty associated with diagnosis of medical disorders while ignoring uncertainty of treatments. To tackle thi...

Spatiotemporal modeling has been received a great attention in both the research and user community. Although considerable research efforts and valuable results do exists, many of the studies and proposed approaches are based on the assumption that objects have crisp boundaries, relationships among them are precisely defined, and accurate measurement of positions leads to error-free representat...

We investigate the meaning of “statistical methods” for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to “geometric fitting” and “geometric model selection”. We point out that a correspondence exists between the standard statistical analysis and the...

ABSTRACT An approach is presented both theoretically and experimentally which overcomes a number of existing conceptual and performance problems in density estimation. The theoretical approach shows methods for incorporating or estimating uncertainties into speech recognition. In the MMI and ML case, precise formulae are given for estimation of densities for uncertainty variances small compared...

This paper presents a method by which uncertainty in reservoir modeling can be reduced by integrating observed data in the estimation of model parameters. The problem is inverse and ill-posed. Further, since the observed data and the reservoir properties are non-linearly related, reducing uncertainty becomes a non-trivial task. The methodology involves generating multiple reservoir descriptions...

This expository article discusses approaches for modeling optimization problems that involve uncertainty. The emphasis of the paper is on motivation and intuition rather than technical completeness. By virtue of its length, the paper is inevitably incomplete, and the reader is advised to look at other papers in this encyclopedia to explore the richness of modeling approaches that this paper can...

Background: One of the most fundamental objectives of the macroeconomic policies is to realize the relationship between economic growth and inflation. According to some monetary policy advisors, inflation reflects erosion in consumer’s purchasing power. Inflation as an important economic variable, affect the economic growth and its impact on economic growth has been proposed in various theories...

Traditional computable general equilibrium (CGE) models have ignored uncertainty ─ even when applied to fields such as environmental modeling that are replete with economic uncertainty. In contrast, many control theory models have focused on the effects of uncertainty. Thus marrying the tradition of CGE and control modeling can result in pricequantity models with explicit dynamics and careful t...

Real-world databases experience various data quality problems of different causes including heterogeneity of consolidated data sources, imprecision of reading devices, and data entry errors. Existence of duplicate records is a prominent data quality problem. The process of duplicate elimination often involves uncertainty in deciding on the true duplicates. Current tools resolve such uncertainty...

Information Source Tracking (IST) method has been developed recently for the modeling and manipulation of uncertain and inaccurate data in relational databases. In this paper we extend the IST method to deductive databases. We show that positive uncertain databases, i.e. IST-based deductive databases with only positive literals in the heads and the bodies of the rules, enjoy a least model/least...