Exponential random graph models (ERGMs) are a well-established family of statistical models for analyzing social networks. Computational complexity has so far limited the appeal of ERGMs for the analysis of large social networks. Efficient computational methods are highly desirable in order to extend the empirical scope of ERGMs. In this paper we report results of a research project on the deve...

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on pre...

This chapter describes a nonparametric Bayesian approach to the estimation of curves and surfaces that act as parameters in statistical models. The approach is based on mixing variable dimensional piecewise constant approximations, whose ‘smoothness’ is regulated by a Markov random field prior. Random partitions of the domain are defined by Voronoi tessellations of random generating point patte...

Identification of causal structures and quantification of direct information flows in complex systems is a challenging yet important task, with practical applications in many fields. Data generated by dynamical processes or large-scale systems are often symbolized, either because of the finite resolution of the measurement apparatus, or because of the need of statistical estimation. By algorith...

This paper introduces a class ofrobust lag-ksmoothersbased on simple low order Markov models for the Gaussian trend-like component of signal plus nonGaussian noise models. The kth order Markov models are of the kth difference form AkX t = €t where Llxt = Xt Xt-l and €t is a zero-mean white Gaussian noise process with variance <1;. The nominal additive noise is a zero-mean white Gaussian noise s...

background: routinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. we aimed at using a hidden markov model in order to detect the abnormal states of pulmonary tuberculosis in iran. methods: data for this study were the weekly number of newly diagnosed cases with sputum smear-positive p...

A method is proposed for efficient scalability in predictive coding, which overcomes known fundamental shortcomings of the prediction loop at enhancement layers. The compression efficiency of an enhancement-layer is substantially improved by casting the design of its prediction module within an estimation-theoretic framework, and thereby exploiting all information available at that layer for th...

We study estimation in the class of stationary variable length Markov Ž . chains VLMC on a finite space. The processes in this class are still Markovian of high order, but with memory of variable length yielding a much bigger and structurally richer class of models than ordinary highorder Markov chains. From an algorithmic view, the VLMC model class has attracted interest in information theory ...