Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

We explore which types of probabilistic updating commute with convex IP pooling (Stewart and Ojea Quintana, 2017). Positive results are stated for Bayesian conditionalization (and a mild generalization of it), imaging, and a certain parameterization of Jeffrey conditioning. This last observation is obtained with the help of a slight generalization of a characterization of (precise) externally B...

Abstract Mardia–Watson–Wheeler is applied for testing whether the circulation observations differ significantly or not. The existing test when all are determined, precise, and certain. In this paper, introduced under neutrosophic statistics. proposed applies circular data not exact, in interval, imprecise indeterminate. statistic of extended decision criteria null hypothesis explained with help...

Commonsense causal reasoning occupies a central position in human reasoning. It plays an essential role in both informal and formal human decision-making. Causality itself as well as human understanding of causality is imprecise, sometimes necessarily so. Our common sense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. A difficulty...

Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, w...

We consider the problem of regression analysis with imprecise data. By imprecise data we mean imprecise observations of precise quantities in the form of sets of values. In this paper, we explore a recently introduced likelihood-based approach to regression with such data. The approach is very general, since it covers all kinds of imprecise data (i.e. not only intervals) and it is not restricte...

If the measurement of production in a commons is accurate and precise, it is possible to design a tradable permit program such that, under a fairly general set of conditions, the market equilibrium is efficient for the given aggregate permit level and everyone is better off after the permit program than before. Often, however, implementation of a tradable permit system is postponed or never und...

The paper presents an efficient solution to decision problems where direct partial information on the distribution of the states of nature is available, either by observations of previous repetitions of the decision problem or by direct expert judgements. To process this information we use a recent generalization of Walley’s imprecise Dirichlet model, allowing us also to handle incomplete obser...