In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior mode (HMPM) estimation on directed graphs with nonca...

Meteorological stations usually contain some missing data for different reasons.There are several traditional methods for completing data, among them bivariate and multivariate linear and non-linear correlation analysis, double mass curve, ratio and difference methods, moving average and probability density functions are commonly used. In this paper a blended model comprising the bivariate expo...

We present a simulation methodology for Bayesian estimation of rate parameters in Markov jump processes arising for example in stochastic kinetic models. To handle the problem of missing components and measurement errors in observed data, we embed the Markov jump process into the framework of a general state space model. We do not use diffusion approximations. Markov chain Monte Carlo and parti...

In this paper, we describe a method of generating harmonic progressions using case-based analysis of existing material that employs a variable-order Markov model. Using a unique method for quantifying harmonic complexity and tension between chord transitions, as well as specifying a desired bass-line, the user specifies a 3 dimensional vector, which the realtime generative algorithm attempts to...

This work deals with unsupervised sonar image segmenta-tion. We present a new estimation segmentation procedure using the recent iterative method of estimation called Iterative Conditional Estimation (ICE) 1]. This method takes into account the variety of the laws in the distribution mixture of a sonar image and the estimation of the parameters of the label eld (modeled by a Markov Random Field...

Although copulas are used and defined for various infinite-dimensional objects (e.g., Gaussian processes Markov processes), there is no prevalent notion of a copula that unifies these concepts. We propose unified functional analytic framework, show how Sklar's theorem can be applied in certain examples Banach spaces provide semiparametric estimation procedure second-order stochastic with underl...

This article presents the state estimation method based on Monte Carlo sampling in a partially observable situation. We formulate an automatic strategy acquisition problem for the multi-agent card game “Hearts” as a reinforcement learning (RL) problem. Since there are often a lot of unobservable cards in this game, RL is dealt with in the framework of a partially observable Markov decision proc...

Although the concept of Batch Markovian Arrival Processes (BMAPs) has gained widespread use in stochastic modelling of communication systems and other application areas, there are few statistical methods of parameter estimation proposed yet. However, in order to practically use BMAPs for modelling, statistical model fitting from empirical time series is an essential task. The present paper cont...