Divergences of Gauss-Markov random fields with application to statistical inference
The central limit theorem for random fields
Asymptotic results in parameter estimation for Gibbs random fields
Estimating interactions in binary data sequences
An approximation of the pressure for the two-dimensional Ising model
Asymptotic theory of parameter estimation for Gauss-Markov random fields
Test for submodel in Gibbs-Markov binary random sequence
Estimating interactions in binary lattice data with nearest-neighbor property
Asymptotic Rényi distances for random fields: properties and applications
The approach introduced in Janžura [Janzura 1997] is further developed and the asymptotic Rényi distances are studied mostly from the point of their monotonicity properties. The results are applied to the problems of statistical inference.
On the concept of the asymptotic Rényi distances for random fields
The asymptotic Rényi distances are explicitly defined and rigorously studied for a convenient class of Gibbs random fields, which are introduced as a natural infinite-dimensional generalization of exponential distributions.
Marginal problem, statistical estimation, and Möbius formula
A solution to the marginal problem is obtained in a form of parametric exponential (Gibbs–Markov) distribution, where the unknown parameters are obtained by an optimization procedure that agrees with the maximum likelihood (ML) estimate. With respect to a difficult performance of the method we propose also an alternative approach, providing the original basis of marginals can be appropriately extended. Then the (numerically feasible) solution can be obtained either by the maximum pseudo-likelihood...
An efficient estimator for Gibbs random fields
An efficient estimator for the expectation is constructed, where is a Gibbs random field, and is a local statistic, i. e. a functional depending on a finite number of coordinates. The estimator coincides with the empirical estimator under the conditions stated in Greenwood and Wefelmeyer [6], and covers the known special cases, namely the von Mises statistic for the i.i.d. underlying fields and the case of one-dimensional Markov chains.
A method for knowledge integration
With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.
Editorial: Data–Algorithms–Decision Making
In memoriam of Igor Vajda
Prognosis and optimization of homogeneous Markov message handling networks
Message handling systems with finitely many servers are mathematically described as homogeneous Markov networks. For hierarchic networks is found a recursive algorithm evaluating after finitely many steps all steady state parameters. Applications to optimization of the system design and management are discussed, as well as a program product 5P (Program for Prognosis of Performance Parameters and Problems) based on the presented theoretical conclusions. The theoretic achievements as well as the practical...
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