This paper considers the problem of continuous state estimation in the presence of random switches. There are three random chains ) ..., , ( 1 1 N N X X X = , ) ..., , ( 1 1 N N R R R = , and ) ..., , ( 1 1 N N Y Y Y = . The random variables i X , i Y , and i R take their values from q R , m R , and { } s S ..., , 1 = , respectively. N Y1 is observed, N X1 and N R1 are hidden, and the problem i...

As real-time collaboration becomes more frequent, it is common for a group of users to create and own an object jointly. The use of multi-user tools makes the existence of jointlyowned objects a necessity: a participant who joins a multi-uscr tool written by others knows that the user agent executed in his name is not a Trojan horse if the multi-user tool is jointly owned by all the participant...

We consider voting over combinatorial domains, where alternatives are binary tuples. We assume that votes are specified as conditionally lexicographic preferences, or LP trees. We study aggregation problems of LP tree votes for several positional scoring rules. Our main goal is to demonstrate that answer-set programming can be effective in solving the winner and the evaluation problems for inst...

In the usual statistical approach of spatial classification, it is assumed that the feature observations are independent conditionally on class labels (conditional independence). Discarding this popular assumption, we consider the problem of statistical classification by using multivariate stationary Gaussian Random Field (GRF) for modeling the conditional distribution given class labels of fea...

The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ‘‘noisy version’’. The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y conditionally on X is a ‘‘long dependen...

Abstract Conditionally specified models are often used to describe complex multivariate data. Such assume implicit structures on the extremes. So far, no methodology exists for calculating extremal characteristics of conditional since copula and marginals not expressed in closed forms. We consider bivariate that specify distribution X Y . provide tools quantify assumptions extremes this class m...

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This paper develops a new nonparametric methodology for monitoring location parame...

Gaussian Markov random fields (GMRFs) are specified conditionally by its precision matrix meaning that its inverse, the covariance matrix, is not explicitly known. Computing the often dense covariance matrix directly using matrix inversion is often unfeasible due to time and memory requirement. In this note, we discuss a simple and fast algorithm to compute the marginal variances for a GMRF. We...