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TimeFrequency Representations of NonStationary Processes
"... Online bayesian inference in some timefrequency representations of nonstationary processes ABCDE ..."
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Online bayesian inference in some timefrequency representations of nonstationary processes ABCDE
Sequence prediction for nonstationary processes
 In proceedings: Combinatorial and Algorithmic Foundations of Pattern and Association Discovery Dagstuhl Seminar
, 2006
"... 1 Suppose we are given two probability measures on the set of oneway infinite finitealphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequenc ..."
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Cited by 16 (11 self)
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1 Suppose we are given two probability measures on the set of oneway infinite finitealphabet sequences. Consider the question when one of the measures predicts the other, that is, when conditional probabilities converge (in a certain sense), if one of the measures is chosen to generate the sequence. This question may be considered a refinement of the problem of sequence prediction in its most general formulation: for a given class of probability measures, does there exist a measure which predicts all of the measures in the class? To address this problem, we find some conditions on local absolute continuity which are sufficient for prediction and generalize several different notions that are known to be sufficient for prediction. We also formulate some open questions to outline a direction for finding the conditions on classes of measures for which prediction is possible.
Extending Observable Operator Models for Nonstationary Processes
, 2007
"... Observable Operator Models (OOMs) are mathematical models of stochastic processes. They have been used successfully to describe sequences of symbols, that is stationary, finitevalued, discretetime stochastic processes. Their high efficiency, the accuracy of the resulting models, together with thei ..."
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Cited by 1 (0 self)
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the learning algorithms for OOMs to the larger class of nonstationary processes by analyzing the properties of OOMs for nonstationary sequences and by adapting the basic version of the OOM learning algorithm to this kind of processes. This task is important because it would help unleash the potential of OOMs
Hybrid Spatial Modeling of NonStationary Process Variations
"... Accurate characterization of spatial variation is essential for statistical performance analysis and modeling, postsilicon tuning, and yield analysis. Existing approaches for spatial modeling either assume that: (i) nonstationarities exist due to a smoothly varying trend component or that (ii) t ..."
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) the process is stationary within regions associated with a predefined grid. While such assumptions may hold when profiling certain classes of variations, many studies now suggest that spatial variability is likely to be highly nonstationary. In order to provide spatial models for nonstationary process varia
Article An Entropy Measure of NonStationary Processes
, 2014
"... www.mdpi.com/journal/entropy ..."
Treatment of geophysical data as a nonstationary process
"... Abstract. The KalmanBucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic nonstationary. The derivation of the estimator is based o ..."
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Abstract. The KalmanBucy method is here analized and applied to the solution of a specific filtering problem to increase the signal message/noise ratio. The method is a time domain treatment of a geophysical process classified as stochastic nonstationary. The derivation of the estimator is based
Modeling of NonStationary Process by Modular Separation of Stability and Plasticity
"... Proc. 1998 IJCNN, Anchorage, Alaska, Vol. 1, pp. 199204, May 1998 In this contribution we present a method for modeling a nonstationary process by a combination of fast learning and slowly learning modules, where the fast learning modules transform the input and output data for stable kernel modul ..."
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Proc. 1998 IJCNN, Anchorage, Alaska, Vol. 1, pp. 199204, May 1998 In this contribution we present a method for modeling a nonstationary process by a combination of fast learning and slowly learning modules, where the fast learning modules transform the input and output data for stable kernel
Wavelet Analysis and Covariance Structure of Some Classes of NonStationary Processes
"... : We study four classes of nonstationary processes: processes with stationary n increments, processes with stationary fractional increments, locally stationary processes and processes with locally stationary nincrements. We establish a simple characterisation of each class of processes by means ..."
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Cited by 1 (1 self)
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: We study four classes of nonstationary processes: processes with stationary n increments, processes with stationary fractional increments, locally stationary processes and processes with locally stationary nincrements. We establish a simple characterisation of each class of processes
ADAPTIVE SELFTUNING UP MODEL FOR NONSTATIONARY PROCESS SIMULATION
"... Abstract. Methodology of nonlinear and nonstationary process simulation, using MATLAB subprogramSIMULINK, is expounded and justified. A self tuning up model to simulate the transient process of a nonlinear and nonstationary electrical heater with variable electrical resistance, as a function of ..."
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Abstract. Methodology of nonlinear and nonstationary process simulation, using MATLAB subprogramSIMULINK, is expounded and justified. A self tuning up model to simulate the transient process of a nonlinear and nonstationary electrical heater with variable electrical resistance, as a function
Eigenspace Updating for NonStationary Process and Its Application to Face Recognition
, 2002
"... In this paper, we introduce a novel approach to modeling nonstationary random processes. Given a set of training samples sequentially, we can iteratively update an eigenspace to manifest the current statistics provided by each new sample. The updated eigenspace is derived more from recent samples a ..."
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Cited by 23 (3 self)
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In this paper, we introduce a novel approach to modeling nonstationary random processes. Given a set of training samples sequentially, we can iteratively update an eigenspace to manifest the current statistics provided by each new sample. The updated eigenspace is derived more from recent samples
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