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Bernat, Andrew

Publications and source records attributed to Bernat, Andrew.

Interval estimates for closure-phase and closure-amplitude imaging in radio astronomy

Interval estimates for closure-phase and closure-amplitude imaging that enable the reconstruction of a radioimage from results of approximate measurements are presented. If the intervals for the measured values are known, the precision of the result of the reconstruction cannot be solved by standard interval methods, because the phase value is based on a circle but not on a real line. If the phase theta (x bar) is measured with precision epsilon, so that the closure phase theta (x bar) + theta (y bar) - theta (x bar + y bar) is known with precision 3 epsilon, then from these measurements theta can be reconstructed with precision 6 epsilon. Similar estimates are given for closure amplitude.

Kreinovich, Vladik

Monte-Carlo methods make Dempster-Shafer formalism feasible

One of the main obstacles to the applications of Dempster-Shafer formalism is its computational complexity. If we combine m different pieces of knowledge, then in general case we have to perform up to 2(sup m) computational steps, which for large m is infeasible. For several important cases algorithms with smaller running time were proposed. We prove, however, that if we want to compute the belief bel(Q) in any given query Q, then exponential time is inevitable. It is still inevitable, if we want to compute bel(Q) with given precision epsilon. This restriction corresponds to the natural idea that since initial masses are known only approximately, there is no sense in trying to compute bel(Q) precisely. A further idea is that there is always some doubt in the whole knowledge, so there is always a probability p(sub o) that the expert's knowledge is wrong. In view of that it is sufficient to have an algorithm that gives a correct answer a probability greater than 1-p(sub o). If we use the original Dempster's combination rule, this possibility diminishes the running time, but still leaves the problem infeasible in the general case. We show that for the alternative combination rules proposed by Smets and Yager feasible methods exist. We also show how these methods can be parallelized, and what parallelization model fits this problem best.

Kreinovich, Vladik YA.

Parallel computers - Estimate errors caused by imprecise data

A new approach to the problem of estimating errors caused by imprecise data is proposed in the context of software engineering. A software device is used to produce an ideal solution to the problem, when the computer is capable of computing errors of arbitrary programs. The software engineering aspect of this problem is to describe a device for computing the error estimates in software terms and then to provide precise numbers with error estimates to the user. The feasibility of the program capable of computing both some quantity and its error estimate in the range of possible measurement errors is demonstrated.

Kreinovich, Vladik