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Diffusion MCMC for Mixture Estimation
Reichl, Jan; Dedecius, Kamil
2016 - English
Distributed inference of parameters of mixture models by a network of cooperating nodes (sensors) with computational and communication capabilities still represents a challenging task. In the last decade, several methods were proposed to solve this issue, predominantly formulated within the expectation-maximization framework and with the assumption of mixture components normality. The present paper adopts the Bayesian approach to inference of general (non-normal) mixtures via the Markov chain Monte Carlo simulation from the parameter posterior distribution. By collaborative tuning of node chains, the method allows reliable estimation even at nodes with significantly worse observational conditions, where the components may tend to merge due to high variances. The method runs in the diffusion networks, where the nodes communicate only with their adjacent neighbors within 1 hop distance.
Keywords:
Mixture; mixture estimation; MCMC
Fulltext is available at external website.
Diffusion MCMC for Mixture Estimation
Distributed inference of parameters of mixture models by a network of cooperating nodes (sensors) with computational and communication capabilities still represents a challenging task. In the last ...
Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Šembera, Ondřej; Tichavský, Petr; Koldovský, Zbyněk
2016 - English
In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.
Keywords:
blind separation; algorithms; block gaussian separation
Fulltext is available at external website.
Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in ...
Basic facts concerning extreme supermodular functions
Studený, Milan
2016 - English
Elementary facts and observations on the cone of supermodular set functions are recalled. The manuscript deals with such operations with set functions which preserve supermodularity\nand the emphasis is put on those such operations which even preserve extremality (of a supermodular function). These involve a few self-transformations of the cone of supermodular set functions. Moreover, projections to the (less-dimensional) linear space of set functions for a subset of the variable set are discussed. Finally, several extensions to the (more-dimensional) linear space of set functions for a superset of the variable set are shown to be both preserving supermodularity and extremality.
Keywords:
supermodular function; standardizations; extreme supermodular function
Fulltext is available at external website.
Basic facts concerning extreme supermodular functions
Elementary facts and observations on the cone of supermodular set functions are recalled. The manuscript deals with such operations with set functions which preserve supermodularity\nand the emphasis ...
Cut Languages in Rational Bases
Šíma, Jiří; Savický, Petr
2016 - English
We introduce a so-called cut language which contains the representations of numbers in a rational base that are less than a given threshold. The cut languages can be used to refine the analysis of neural net models between integer and rational weights. We prove a necessary and sufficient condition when a cut language is regular, which is based on the concept of a quasi-periodic power series. We show that any cut language with a rational threshold is context-sensitive while examples of non-context-free cut languages are presented.
Keywords:
cut language; rational base; quassi-periodic power series
Available in a digital repository NRGL
Cut Languages in Rational Bases
We introduce a so-called cut language which contains the representations of numbers in a rational base that are less than a given threshold. The cut languages can be used to refine the analysis of ...
Interval Matrices: Regularity Yields Singularity
Rohn, Jiří
2016 - English
It is proved that regularity of an interval matrix implies singularity of two related interval matrices.
Keywords:
interval matrix; regularity; singularity
Available in a digital repository NRGL
Interval Matrices: Regularity Yields Singularity
It is proved that regularity of an interval matrix implies singularity of two related interval matrices.
ALMA Development Plan Study: Solar Research with ALMA. Progress Report
Brajša, R.; Bárta, Miroslav; Skokić, Ivica; Bastian, T.S.; Shimojo, M.; White, S. M.; Iwai, K.
2016 - English
A mid-term progress report of the research/development project Solar Research with ALMA: Development study. The project has been demanded by ESO to the Czech node of the European ALMA Regional Center. The main goal of the project is development of the specific Solar Observing Mode for ALMA observatory. The report on demand of ESO summarizes results of the WP2: Observing modes and ALMA software requirements. Technical ALMA requirements of solar observing mode are studied and summarised. The ALMA user software was reviewed and its changes complying with the Solar Observing Mode has been proposed. The report has been reviewed by international expert panel nominated by ESO.\n\n\n
Keywords:
ALMA; solar research; observing modes
Fulltext is available at external website.
ALMA Development Plan Study: Solar Research with ALMA. Progress Report
A mid-term progress report of the research/development project Solar Research with ALMA: Development study. The project has been demanded by ESO to the Czech node of the European ALMA Regional Center. ...
Comparison of mixture-based classification with the data-dependent pointer model for various types of components
Likhonina, Raissa; Suzdaleva, Evgenia; Nagy, Ivan
2016 - English
The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model.
Keywords:
mixture-based classification; data-dependent pointer; recurisive mixture estimation
Fulltext is available at external website.
Comparison of mixture-based classification with the data-dependent pointer model for various types of components
The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of ...
Linear ARX and state-space model with uniform noise: computation of first and second moments
Jirsa, Ladislav
2016 - English
This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown parameters and states of linear model with uniformly distributed noise.
Keywords:
uncertainty; bounded variable; uniform noise; linear model; model identification; state estimation
Fulltext is available at external website.
Linear ARX and state-space model with uniform noise: computation of first and second moments
This report collects technical procedures used for computations of various estimates and keeps them in one place for internal purposes. The context concerns application of estimation of unknown ...
On Exact Heteroscedasticity Testing for Robust Regression
Kalina, Jan; Peštová, Barbora
2016 - English
The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also the asymptotic behavior of the permutation test statistics of the Goldfeld-Quandt and Breusch-Pagan tests is investigated. A numerical experiment on real economic data is presented, which also shows how to perform a robust prediction model under heteroscedasticity.
Keywords:
robust estimation; outliers; variance; diagnostic tools; heteroscedasticity
Available in digital repository of the ASCR
On Exact Heteroscedasticity Testing for Robust Regression
The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also ...
Robust Regularized Discriminant Analysis Based on Implicit Weighting
Kalina, Jan; Hlinka, Jaroslav
2016 - English
In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables exceeding the number of observations. However, its various available versions are too vulnerable to the presence of outlying measurements in the data. In this paper, we exploit principles of robust statistics to propose new versions of regularized linear discriminant analysis suitable for highdimensional data contaminated by (more or less) severe outliers. The work exploits a regularized version of the minimum weighted covariance determinant estimator, which is one of highly robust estimators of multivariate location and scatter. The performance of the novel classification methods is illustrated on real data sets with a detailed analysis of data from brain activity research.
Keywords:
high-dimensional data; classification analysis; robustness; outliers; regularization
Available in a digital repository NRGL
Robust Regularized Discriminant Analysis Based on Implicit Weighting
In bioinformatics, regularized linear discriminant analysis is commonly used as a tool for supervised classification problems tailormade for high-dimensional data with the number of variables ...
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