Results 1  10
of
39
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 ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
(Show Context)
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
Sequential Sampling Techniques for LogNormal Combination of Probability Densities
"... Ústav teorie informace a automatizace, v.v.i. ..."
(Show Context)
On Estimation of Unknown Disturbances of NonLinear StateSpace Model Using Marginalized Particle Filter
"... Ústav teorie informace a automatizace, v.v.i. ..."
(Show Context)
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 ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
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.
Adaptive Importance Sampling in Particle Filtering
 In Information Fusion, 16th International Conference on (FUSION), 2013
"... Abstract—Computational efficiency of the particle filter, as a method based on importance sampling, depends on the choice of the proposal density. Various default schemes, such as the bootstrap proposal, can be very inefficient in demanding applications. Adaptive particle filtering is a general clas ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Computational efficiency of the particle filter, as a method based on importance sampling, depends on the choice of the proposal density. Various default schemes, such as the bootstrap proposal, can be very inefficient in demanding applications. Adaptive particle filtering is a general class of algorithms that adapt the proposal function using the observed data. Adaptive importance sampling is a technique based on parametrization of the proposal and recursive estimation of the parameters. In this paper, we investigate the use of the adaptive importance sampling in the context of particle filtering. Specifically, we propose and test several options of parameter initialization and particle association. The technique is applied in a demanding scenario of tracking an atmospheric release of radiation. In this scenario, the likelihood of the observations is rather sharp and its evaluation is computationally expensive. Hence, the overhead of the adaptation procedure is negligible and the proposed adaptive technique clearly improves over nonadaptive methods. I.
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 ..."
Abstract
 Add to MetaCart
(Show Context)
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.
BAYESIAN PERIODOGRAM SMOOTHING FOR SPEECH ENHANCEMENT
"... Abstract. Periodogram smoothing of the received noisy signal is a challenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sufficient statistics using new samples of the noisy signa ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. Periodogram smoothing of the received noisy signal is a challenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sufficient statistics using new samples of the noisy signal, the smoothing parameter is adjusted online. The performance of the novel smoothing algorithm is studied in a speech enhancement context. It is demonstrated that with respect to Mean Square Error, the proposed Bayesian smoothing algorithm performs better than the other nonBayesian smoothing algorithms in higher signaltonoise ratio environments. 1