This paper proposes a new framework of choice under uncertainty, where the only information available to the decision maker is about the the ordinal likelihood of the di erent outcomes each action generates. This contrasts both with the classical models where the potential outcomes of each action have an associated probability distribution, and with the more recent complete uncertainty models, ...

The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been wide...

Mashup is a new kind integration application and users can compose related services as components to build new application—the mashups. Now the services on the web have different degrees of data uncertainty, including data error, stale data, and improper data processing and so on. We provide a data uncertainty model for mashup component which is assessed in the space of the homogeneous componen...

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...

Rough data envelopment analysis (RDEA) evaluates the performance of the decision making units (DMUs) under rough uncertainty assumption. In this paper, new discussion regarding RDEA is extended. The RSBM model is proposed by integrating SBM model and rough set theory. The process of reaching solution is presented and this model is applied to efficiency evaluation of the DMUs with uncertain ...

Geographical data is inherently uncertain. There are a variety of sources that contribute to this uncertainty. Operations performed on data during an analysis will most likely only increase this uncertainty. To understand the uncertainty in the results of an analysis, requires information about the uncertainty in the input data, and some idea about how various analysis operations propagate and ...

Although uncertainties exist in spatial data mining, they have not been paid much attention to. Uncertainty may have an influence on the confidential level, supportable level, and interesting level of spatial data mining. This paper proposes uncertainty-based spatial data mining. First, the concept is given in the integrated contexts of both uncertainty and spatial data mining. The inherent unc...

Many dynamic planning and management problems are typically characterised by a level of uncertainty regarding the value of data input such as supply and demand patterns. Assigning inaccurate values to them could invalidate the results of the study. Consequently, deterministic models are inadequate for the representation of these problems where the most crucial parameters are either unknown or a...

on account of the existence of uncertainty, dea occasionally faces the situation of imprecise data, especially when a set of dmus include missing data, ordinal data, interval data, stochastic data, or fuzzy data. therefore, how to evaluate the efficiency of a set of dmus in interval environments is a problem worth studying. in this paper, we discussed the new method for evaluation and ranking i...

background: this study aimed at estimating the proportion of diabetes as a risk factor to the attributable burden of cardiovascular diseases in iran. methods: comparative risk assessment methodology was used to calculating potential impact fraction (pif). to calculate pif, data on the prevalence of newly diagnosed diabetes mellitus (ndm) and known diabetes mellitus (kdm) were obtained from 3rd ...