A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching. But all these methods are applicable at the time of study design. When experimental designs are premature, impractical, or impossible, resear...

Recent applications of SPICE–PAC to transistor parameter extraction and hierarchical simulation require rather flexible circuit analyses, performed for irregularly distributed values of independent variables. An implementation of table–driven analyses (DC, AC and time–domain) is described in which tables of arbitrarily distributed independent variables (voltages, frequencies or timepoints) are ...

Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. W...

In this article it is shown how non-holonomic constraints can be included in the formulation of the dynamic equations of flexible multibody systems. The equations are given in state space form with the degrees of freedom, their derivatives and the kinematic coordinates as state variables, which circumvents the use of Lagrangian multipliers. With these independent state variables for the system ...

The goal of the paper is to propose a semistructured data model for representing XML documents and a language for querying semistructured database representing XML data. The language is based on a path calculus and on its extension involving rules (in Datalog style) and Skolem functions. Three kinds of matching between query variables and database objects are discussed: a rigid, semirigid, and ...

The AMIDST Toolbox is a software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed implementations of Bayesian learning algorith...

H∞ controller for active magnetic bearings (AMBs) with flexible rotor system was designed in this paper. The motion equations of AMBs and flexible rotor system are built based on finite element methods (FEM). Weighting function matrices of H∞ controller for AMBs are studied for both the sensitivity and the complementary sensitivity of H∞ control theory. The simulation shows that the H∞ control ...

In this paper we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are g...

Finding common latent components from data is an important step in many data mining applications. These latent variables are typically categorical and there are many sources of categorical variables, including dichotomous, nominal, ordinal, and cardinal values. Thus it is important to be able to represent the discrete components (categories) in a flexible way. Here we propose a nonparametric Ba...