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35
Identification of Reality in Bayesian Context
 ComputerIntensive Methods in Control and Signal Processing: Curse of Dimensionality. Birkhauser
, 1997
"... Complexity has many facets as does any general concept. The relationship between "infinitely" complex reality and restricted complexity of the artificial world of models is addressed. Particularly, the paper tries to clarify the meaning of Bayesian identification under mismodelling by answ ..."
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Complexity has many facets as does any general concept. The relationship between "infinitely" complex reality and restricted complexity of the artificial world of models is addressed. Particularly, the paper tries to clarify the meaning of Bayesian identification under mismodelling by answering the question, "What is the outcome of the Bayesian identification without supposing the model set considered contains the "true" system model?" The answer relates known asympotic results to the "natural" finitetime domain of Bayesian paradigm. It serves as an interpretation "smoother" of those Bayesian identification results that quietly ignore the mismodelling present. Keywords: decisionmaking, model selection, Bayesian identification, approximation 1 Introduction System identification can be understood as the set of procedures which model an investigated part of reality (called object, process, plant or system) using data measured on it [8]. Modelling of the reality, often informal, is a n...
Bayes for rolling mills: From parameter estimation to decision support
 In Proceedings of the 16th IFAC World Congress
, 2005
"... Abstract: The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as applicationoriented solutions developed. Specifically, the ..."
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Abstract: The paper shares experience gained in application of dynamic Bayesian approach to control problems in the field of metal rolling. The contribution introduces basic notions of theory applied and provides the algorithmic as well as applicationoriented solutions developed. Specifically, the consistent use of the approach resulted in an advanced decision support system for operators of a reversing cold rolling mill.
On entrywise organized filtering
 in Proceedings of 15th International Conference on Process Control’05, High Tatras
"... Abstract: The paper deals with state estimation in a factorized form. By the concept of the factorized filtering we mean such data preprocessing, as a result of which the ndimensional statespace model can be decomposed into n onedimensional models (factors). The key idea is the factors are expect ..."
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Abstract: The paper deals with state estimation in a factorized form. By the concept of the factorized filtering we mean such data preprocessing, as a result of which the ndimensional statespace model can be decomposed into n onedimensional models (factors). The key idea is the factors are expected to open a way to describe jointly continuous and discrete probability distributions. A special transformation of the statespace model is considered. The general solution of the factorized state estimation is discussed.
TOWARDS STATE ESTIMATION IN THE FACTORIZED FORM
"... Abstract. The paper addresses the state estimation in the factorized form. The target application area is the urban traffic control, where the main controlled variables (queues) are not directly observable and have to be estimated. Additional problem is that some state variables are of a discreteva ..."
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Abstract. The paper addresses the state estimation in the factorized form. The target application area is the urban traffic control, where the main controlled variables (queues) are not directly observable and have to be estimated. Additional problem is that some state variables are of a discretevalued nature. Thus, estimation of mixedtype data (continuous and discrete valued) models is highly desirable. Factorized state estimation is a potential solution of this problem. The underlying methodology is Bayesian filtering. Factorized version of the filter is obtained by applying the chain rule to the statespace model. The general solution represents the recursive entrywise performance of data updating and time updating steps. Application of the solution to linear Gaussian statespace models gives the factorized Kalman filter. 1
On dual expression of prior information in Bayesian parameter estimation
 Preprints of the 11th IFAC Symposium on System Identification
, 1997
"... Abstract: In Bayesian parameter estimation, a priori information can be used to shape the prior density of unknown parameters of the model. When chosen in a conjugate, selfreproducing form, the prior density of parameters is nothing but a modelbased transform of a certain “prior ” density of obser ..."
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Abstract: In Bayesian parameter estimation, a priori information can be used to shape the prior density of unknown parameters of the model. When chosen in a conjugate, selfreproducing form, the prior density of parameters is nothing but a modelbased transform of a certain “prior ” density of observed data. This observation suggests two possible ways of expressing a priori knowledge—in terms of parameters of a particular model and in terms of data entering the model. The latter way turns out useful when dealing with statistical models whose parameters lack a direct physical interpretation. In practice, the amount of a priori information is usually not sufficient for complete specification of the prior density of data. The paper shows an informationbased way of converting such incomplete information into the prior density of unknown parameters.
Sequential Sampling Techniques for LogNormal Combination of Probability Densities
"... Ústav teorie informace a automatizace, v.v.i. ..."
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QUO VADIS, BAYESIAN IDENTIFICATION?
"... The Bayesian identification of nonlinear, nonGaussian, nonstationary or nonparametric models is notoriously known as computerintensive and not solvable in a closed form. The paper outlines three major approaches to approximate Bayesian estimation, based on locally weighted smoothing of data, ite ..."
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The Bayesian identification of nonlinear, nonGaussian, nonstationary or nonparametric models is notoriously known as computerintensive and not solvable in a closed form. The paper outlines three major approaches to approximate Bayesian estimation, based on locally weighted smoothing of data, iterative and noniterative Monte Carlo simulation and direct approximation of an information “distance ” between the empirical and model distributions of data. The informationbased view of estimation is used throughout to give more insight into the methods and show their mutual relationship.
Research Article Implementation of the LeastSquares Lattice with Order and Forgetting Factor Estimation for FPGA
, 2008
"... A high performance RLS lattice filter with the estimation of an unknown order and forgetting factor of identified system was developed and implemented as a PCORE coprocessor for Xilinx EDK. The coprocessor implemented in FPGA hardware can fully exploit parallelisms in the algorithm and remove load f ..."
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A high performance RLS lattice filter with the estimation of an unknown order and forgetting factor of identified system was developed and implemented as a PCORE coprocessor for Xilinx EDK. The coprocessor implemented in FPGA hardware can fully exploit parallelisms in the algorithm and remove load from a microprocessor. The EDK integration allows effective programming and debugging of hardware accelerated DSP applications. The RLS lattice core extended by the order and forgetting factor estimation was implemented using the logarithmic numbers system (LNS) arithmetic. An optimal mapping of the RLS lattice onto the LNS arithmetic units found by the cyclic scheduling was used. The schedule allows us to run four independent filters in parallel on one arithmetic macro set. The coprocessor containing the RLS lattice core is highly configurable. It allows to exploit the modular structure of the RLS lattice filter and construct the pipelined serial connection of filters for even higher performance. It also allows to run independent parallel filters on the same input with different forgetting factors in order to estimate which order and exponential forgetting factor better describe the observed data. The FPGA coprocessor implementation presented in the paper is able to evaluate the RLS lattice filter of order 504 at 12 kHz input data sampling rate. For the filter of order up to 20, the probability of order and forgetting factor hypotheses can be continually estimated. It has been demonstrated that the implemented coprocessor accelerates the Microblaze solution up to 20 times. It has also been shown that the coprocessor performs up to 2.5 times faster than highly optimized solution using 50 MIPS SHARC DSP processor, while the Microblaze is capable of performing another tasks concurrently.
Software Analysis Unifying Particle Filtering and Marginalized Particle Filtering
"... Abstract – Particle filtering has evolved into wide range of techniques giving rise to many implementations and specialized algorithms. In theory, all these techniques are closely related, however this fact is usually ignored in software implementations. In this paper, particle filtering is studied ..."
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Abstract – Particle filtering has evolved into wide range of techniques giving rise to many implementations and specialized algorithms. In theory, all these techniques are closely related, however this fact is usually ignored in software implementations. In this paper, particle filtering is studied together with marginalized particle filtering and a generic software scheme unifying these two areas is proposed. It is presented in general terms of objectoriented programming so that it may be implemented in existing Bayesian filtering toolboxes that are briefly reviewed. The power of the approach is illustrated on a new variant of the marginalized particle filter. A range of new variants of the filter is obtained by plugging this class into the proposed software structure. The framework and the illustrative example is implemented in the BDM library.
THE VARIATIONAL EM ALGORITHM FOR ONLINE IDENTIFICATION OF EXTENDED AR MODELS
"... The AutoRegressive (AR) model is extended to cope with a wide class of possible transformations and degradations. The Variational Bayes (VB) procedure is used to restore conjugacy. The resulting Bayesian recursive identification procedure has many of the desirable computational properties of the cla ..."
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The AutoRegressive (AR) model is extended to cope with a wide class of possible transformations and degradations. The Variational Bayes (VB) procedure is used to restore conjugacy. The resulting Bayesian recursive identification procedure has many of the desirable computational properties of the classical RLS procedure. During each timestep, an iterative Variational EM (VEM) procedure is required to obtain the necessary moments. The procedure is used to reconstruct an outliercorrupted AR process and a noisy speech segment. The VB scheme appears to offer improved performance over the related QuasiBayes (QB) scheme in the case of timevariant component weights. 1.