Number of found documents: 1458
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Prediction of Pedestrian Movement During The Egress Situation
Hrabák, Pavel; Ticháček, O.
2015 - English
The report summarizes the up-to-now progress in the application of the recursive estimation on the prediction of the pedestrian movement during the egress or evacuation situation. For these purposes a simple decision-making model has been introduced taking into account only the forward and sideways movement of pedestrians. Based on this model, a test simulation has been developed in order to test the applicability of the estimation tool to the stated decision-making model. Two main approaches of the decision process incorporated in the simulation are discussed and a modified version of the original model is presented. The report contains a manual to the used Matlab scripts and functions. The codes of needed m-files are incorporated as well. Keywords: Recursive estimation; mixture of Markov chains; pedestrian movement; egress simulation Fulltext is available at external website.
Prediction of Pedestrian Movement During The Egress Situation

The report summarizes the up-to-now progress in the application of the recursive estimation on the prediction of the pedestrian movement during the egress or evacuation situation. For these purposes ...

Hrabák, Pavel; Ticháček, O.
Ústav teorie informace a automatizace, 2015

Normal and uniform noise - violation of the assumption on noise distribution in model identification
Jirsa, Ladislav; Pavelková, Lenka
2015 - English
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has created the basis for theoretical and algorithmic solutions of respective tasks. However, many continuous variables are strictly bounded and their uncertainty may have origin in various physical processes which causes a non-normal distribution of their noise. Furthermore, adaptation of algorithms based on normal model for identification of models with bounded noise can distort the estimates due to inconsistent handling of uncertainty. This report describes a study to compare results of estimation algorithms based on assumption of normal and uniform noise. Data sequences processed by the algorithms have normal noise bounded by a low limit with respect to standard deviation. We illustrate disparity between noise assumption and a true noise distribution and its influence on the quality of the estimates. It is a part of an effort to develop theory and fast algorithms for estimation with bounded noise, applicable in practice. Keywords: uncertainty; bounded variable; uniform noise; model identification; assumption of normal noise; estimation comparison Fulltext is available at external website.
Normal and uniform noise - violation of the assumption on noise distribution in model identification

Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has ...

Jirsa, Ladislav; Pavelková, Lenka
Ústav teorie informace a automatizace, 2015

On Linear Probabilistic Opinion Pooling Based on Kullback-Leibler Divergence
Sečkárová, Vladimíra
2015 - English
In this contribution we focus on the finite collection of sources, providing their opinions about a hidden (stochastic) phenomenon, that is not directly observable. The assumption on obtaining opinions yields a decision making process commonly referred to as opinion pooling. Due to the complexity of the space of possible decisions we consider the probability distributions over this set rather than single values, exploited before, e.g., in [2]. The final decision (result of pooling) is then a combination of probability distributions provided by sources. Keywords: linear opinion pooling; minimum cross-entropy principle; expected Kullback-Leibler divergence Fulltext is available at external website.
On Linear Probabilistic Opinion Pooling Based on Kullback-Leibler Divergence

In this contribution we focus on the finite collection of sources, providing their opinions about a hidden (stochastic) phenomenon, that is not directly observable. The assumption on obtaining ...

Sečkárová, Vladimíra
Ústav teorie informace a automatizace, 2015

Proceedings of the 10th Workshop on Uncertainty Processing
Kratochvíl, Václav
2015 - English
WUPES 2015 is organized jointly by the Institute of Information Theory and Automation of the Czech Academy of Sciences and by the Faculty of Management, University of Economics, Prague. It is quite natural that such a meeting could not materialize if it were not for the hard work of many our colleagues and friends. This is why we want to express our gratitude to all the members of both the Programme and Organizing Committees. Last but not least, we also want to acknowledge the fact that this workshop is organized, due to the fact that the research of several members of the Organizing Committee is financially supported by grants GA CR no 15-00215S and 13-20012S. Keywords: Bayesian networks; uncertainty; optimization Fulltext is available at external website.
Proceedings of the 10th Workshop on Uncertainty Processing

WUPES 2015 is organized jointly by the Institute of Information Theory and Automation of the Czech Academy of Sciences and by the Faculty of Management, University of Economics, Prague. It is quite ...

Kratochvíl, Václav
Ústav teorie informace a automatizace, 2015

Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Tichavský, Petr; Šembera, Ondřej; Koldovský, Zbyněk
2015 - English
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. Keywords: Autoregressive processes; Cramer-Rao bound; Blind source separation Fulltext is available at external website.
Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch

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, ...

Tichavský, Petr; Šembera, Ondřej; Koldovský, Zbyněk
Ústav teorie informace a automatizace, 2015

Model of Risk and Losses of a Multigeneration Mortgage Portfolio
Šmíd, Martin
2015 - English
During the last decades, Merton-Vasicek factor model (1987), later generalize by Frye at al. (2000), became standards in credit risk management. We present a generalization of these models allowing multiple sub-portfolios of loans possibly starting at different times and lasting more than one period. We show that, given this model, a one-to-one mapping between factors and the overall default rate and the charge-off rate exists, is differentiable and numerically computable. Keywords: risk management; loan portfolio; default rate; charge off rate Fulltext is available at external website.
Model of Risk and Losses of a Multigeneration Mortgage Portfolio

During the last decades, Merton-Vasicek factor model (1987), later generalize by Frye at al. (2000), became standards in credit risk management. We present a generalization of these models allowing ...

Šmíd, Martin
Ústav teorie informace a automatizace, 2015

An empirical comparison of popular algorithms for learning gene networks
Djordjilović, V.; Chiogna, M.; Vomlel, Jiří
2015 - English
In this work, we study the performance of different algorithms for learning gene networks from data. We consider representatives of different structure learning approaches, some of which perform unrestricted searches, such as the PC algorithm and the Gobnilp method and some of which introduce prior information on the structure, such as the K2 algorithm. Competing methods are evaluated both in terms of their predictive accuracy and their ability to reconstruct the true underlying network. A real data application based on an experiment performed by the University of Padova is also considered. We also discuss merits and disadvantages of categorizing gene expression measurements. Keywords: Bayesian networks; Gene networks; Biological pathways Fulltext is available at external website.
An empirical comparison of popular algorithms for learning gene networks

In this work, we study the performance of different algorithms for learning gene networks from data. We consider representatives of different structure learning approaches, some of which perform ...

Djordjilović, V.; Chiogna, M.; Vomlel, Jiří
Ústav teorie informace a automatizace, 2015

Algorithms for single-fault troubleshooting with dependent actions
Lín, Václav
2015 - English
We study the problem of single-fault troubleshooting with dependent actions. We propose a binary integer programming formulation for the problem. This can be used to solve the problem directly or to compute lower bounds of optima using linear programming relaxation. We present an optimal dynamic programming algorithm, and three greedy algorithms for computing upper bounds of optima. Keywords: single-fault troubleshooting; algorithms; linear programming relaxation Fulltext is available at external website.
Algorithms for single-fault troubleshooting with dependent actions

We study the problem of single-fault troubleshooting with dependent actions. We propose a binary integer programming formulation for the problem. This can be used to solve the problem directly ...

Lín, Václav
Ústav teorie informace a automatizace, 2015

Second Order Optimality in Transient and Discounted Markov Decision Chains
Sladký, Karel
2015 - English
The article is devoted to second order optimality in Markov decision processes. Attention is primarily focused on the reward variance for discounted models and undiscounted transient models (i.e. where the spectral radius of the transition probability matrix is less than unity). Considering the second order optimality criteria means that in the class of policies maximizing (or minimizing) total expected discounted reward (or undiscounted reward for the transient model) we choose the policy minimizing the total variance. Explicit formulae for calculating the variances for transient and discounted models are reported along with sketches of algoritmic procedures for finding second order optimal policies. Keywords: dynamic programming; discounted and transient Markov reward chains; reward-variance optimality Fulltext is available at external website.
Second Order Optimality in Transient and Discounted Markov Decision Chains

The article is devoted to second order optimality in Markov decision processes. Attention is primarily focused on the reward variance for discounted models and undiscounted transient models (i.e. ...

Sladký, Karel
Ústav teorie informace a automatizace, 2015

Evaluation of Kullback-Leibler Divergence
Homolová, Jitka; Kárný, Miroslav
2015 - English
Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distributions. This technical paper collects its analytical and numerical expressions for the broad range of distributions. Keywords: Kullback-Leibler divergence; cross-entropy; Bayesian decision making; Bayesian learning and approximation Fulltext is available at external website.
Evaluation of Kullback-Leibler Divergence

Kullback-Leibler divergence is a leading measure of similarity or dissimilarity of probability distributions. This technical paper collects its analytical and numerical expressions for the broad range ...

Homolová, Jitka; Kárný, Miroslav
Ústav teorie informace a automatizace, 2015

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