This paper proposes a novel naı̈ve Bayesian classifier in categorical uncertain data streams. Uncertainty in categorical data is usually represented by vector valued discrete pdf, which has to be carefully handled to guarantee the underlying performance in data mining applications. In this paper, we map the probabilistic attribute to deterministic points in the Euclidean space and design a dista...

Uncertainty about the choice of identifying assumptions is common in causal studies, but often ignored empirical practice. This paper considers uncertainty over models that impose different assumptions, which can lead to a mix point‐ and set‐identified models. We propose performing inference presence such by generalizing Bayesian model averaging. The method multiple posteriors for combines them...

When confronted with uncertain prospects, people often exhibit both choice deferral and Ellsberg-type ambiguity aversion. This paper obtains a joint representation for these behavioral phenomena. The decision maker as portrayed by my model is willing to choose an uncertain prospect f over g rather than to defer this choice if and only if the expected utility of f is greater that or equal to the...

OBJECTIVE To measure the uncertainty of temporal assertions like "3 weeks ago" in clinical texts. DESIGN Temporal assertions extracted from narrative clinical reports were compared to facts extracted from a structured clinical database for the same patients. MEASUREMENTS The authors correlated the assertions and the facts to determine the dependence of the uncertainty of the assertions on t...

We call markets in which intermediaries sell networks of suppliers to consumers who are uncertain about their needs "option demand markets." In these markets, suppliers may grant the intermediaries discounts in order to be admitted to their networks. We derive a measure of each supplier's market power within the network; the measure is based on the additional ex ante expected utility consumers ...