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Representations of Bayesian Networks by Low-Rank Models
Tichavský, Petr ; Vomlel, Jiří
Conditional probability tables (CPTs) of discrete valued random variables may achieve high dimensions and Bayesian networks defined as the product of these CPTs may become intractable by conventional methods of BN inference because of their dimensionality. In many cases, however, these probability tables constitute tensors of relatively low rank. Such tensors can be written in the so-called Kruskal form as a sum of rank-one components. Such representation would be equivalent to adding one artificial parent to all random variables and deleting all edges between the variables. The most difficult task is to find such a representation given a set of marginals or CPTs of the random variables under consideration. In the former case, it is a problem of joint canonical polyadic (CP) decomposition of a set of tensors. The latter fitting problem can be solved in a similar manner. We apply a recently proposed alternating direction method of multipliers (ADMM), which assures that the model has a probabilistic interpretation, i.e., that all elements of all factor matrices are nonnegative. We perform experiments with several well-known Bayesian networks.\n\n
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Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Šembera, Ondřej ; Tichavský, Petr ; Koldovský, Zbyněk
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.
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Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Tichavský, Petr ; Šembera, Ondřej ; Koldovský, Zbyněk
Modeling real-world acoustic signals and namely speech signals as piecewise stationary random processes is a possible approach to blind separation of linear mixtures of such signals. In this paper, the piecewise AR(1) modeling is studied and is compared to the more common piecewise AR(0) modeling, which is known under the names Block Gaussian SEParation (BGSEP) and Block Gaussian Likelihood (BGL). The separation based on the AR(0) modeling uses an approximate joint diagonalization (AJD) of covariance matrices of the mixture with lag 0, computed at epochs (intervals) of stationarity of the separated signals. The separation based on the AR(1) modeling uses the covariances of lag 0 and covariances of lag 1 jointly. For this model, we derive an approximate Cram´er-Rao lower bound on the separation accuracy for estimation based on the full set of the statistics (covariance matrices of lag 0 and lag 1) and covariance matrices with lag 0 only. The bounds show the condition when AR(1) modeling leads to significantly improved separation accuracy.
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Návrh na vylepšení marketingové strategie pro kavárnu CrossCafe
Vácha, Pavel ; Postler, Milan (vedoucí práce) ; Tichavský, Petr (oponent)
Cílem této bakalářské práce je popsat franchisingový řetězec kaváren CrossCafe. V práci jsem čerpal jak z teoretických znalostí, tak z praktického výzkumu v terénu. Za pomoci SWOT analýzy, analýzy konkurence, určení typického zákazníka a zhodnocení současné marketingové strategie je navržena nová alternativní strategie, včetně návrhů, jakým směrem by se mohl celý koncept ubírat do budoucna.
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