An analytical solution is presented that reconstructs residual stress field from limited and incomplete data.  The inverse problem of reconstructing residual stresses is solved using an appropriate form of the airy stress function.  This function is chosen to satisfy the stress equilibrium equations together with the boundary conditions for a domain within a convex polygon.  The analytical solu...

Рассмотрена задача идентификации в дискретной системе со скачкообразными параметрами. Алгоритм предполагает использование оценок, построенных с помощью экстраполятора Калмана оценками неизвестных входов и модели вектора наблюдения. Для иллюстрации предлагаемого подхода приведен пример.

A method is proposed to reconstruct signals from incomplete data. The method, which can be interpreted both as a discrete implementation of the so-called prior discrete Fourier transform (PDFT) spectral estimation technique and as a variant of the algebraic reconstruction technique, allows one to incorporate prior information about the reconstructed signal to improve the resolution of the signa...

In the last decade, skyline queries have been extensively studied for different domains because of their wide applications in multi-criteria decision making and search space pruning. A skyline query returns all the interesting points in a multi-dimensional data set that are not dominated by any other point with respect to all dimensions. However, real world data sets are seldom complete, i.e. d...

In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classificat...

In this note we attempt to trace the history and development of Markov chain Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today. We see how the earlier stages of the Monte Carlo (MC, not MCMC) research have led to the algorithms currently in use. More importantly, we see how the development of this methodology has not only changed our solutions to problems, but...

Decision-makers are often met with situations where optimal decisions have to be made in the presence of missing information. To facilitate such work we propose application of Armstrong axioms. Key–Words: Decision support systems, uncertainty management, inference axioms

Graph databases underlie several modern applications such as social networks and the Semantic Web. In those scenarios, integrating and exchanging data is very common, which leads to proliferation of incomplete graph data. However, the well developed models of incompleteness of data do not apply to graph data. This is mainly due to the fact that standard graph query languages concentrate on grap...

Model selection in complete data is a common task for the applied researcher. However, in many scenarios data are incomplete which further complicates the task of model selection. In this talk, we will specify the problem of model selection in incomplete data and discuss several possible solutions using multiple imputation. First, we will define a new general measure for the correct model selec...