3 edition of Singular non-gaussian measures in detection and estimation theory found in the catalog.
Singular non-gaussian measures in detection and estimation theory
Percy A. Pierre
|Statement||[by] Percy A. Pierre.|
|LC Classifications||AS36 .R28 no. 3954, TK5102.5 .R28 no. 3954|
|The Physical Object|
|Number of Pages||29|
|LC Control Number||81483327|
Many condition-monitoring and fault diagnosis techniques have been developed in the last few decades to improve the reliability of rolling element bearings. This chapter provides an overview on the most commonly employed condition-monitoring, signal analysis, and fault diagnosis techniques for rolling element bearings and discusses some of the Cited by: 1. We finally discuss the extension of the method to non-Gaussian estimation problems. Keywords: decision making, value information, mechanical equivalent, Bayes’ theorem, decision parameter estimation. 1 Introduction. Structural engineers usually have a solid background in mechanics, yet not always a good relationship with probability theory. Winner of the Joseph W. Goodman Book Writing Award! A comprehensive treatment of the principles, mathematics, and statistics of image science In today’s visually oriented society, images play an important role in conveying messages. From seismic imaging to satellite images to medical images, our modern society would be lost without images to enhance our understanding of our health, our. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine : Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori.
or non-Gaussian problems there is no general analytic (closed form) expression for the required PDF. The extended Kalman filter (EKF) is the most popular approach to recursive nonlinear estimation Here the estimation problem is linearised about the predicted state so that the Kalman filter can be applied. In this case the.
Imaging the Word
Science makes sense.
The Mexican in Minnesota
Bibliography of Hafnium.
Restructuring of cooperative support services in Asia-Pacific
Proposed Palo Verde-Devers power transmission line
free negro in Mississippi before the Civil War
Beneath the Lion bold
sociology of slavery
LMSS final report and project summary covering the contract period January 1989 to August 1989
A presentation of various sufficient conditions for singular (error-free) signal detection and estimation. For the case of a known signal, second moment conditions are given which imply singularity of detection in the most general kind of noise. For the case of random signals, no such general result exists.
Get this from a library. Singular non-gaussian measures in detection and estimation theory. [Percy A Pierre; Rand Corporation.]. The First Edition of Detection, Estimation, and Modulation Theory, Part I, enjoyed a long useful life.
However, in the forty-four years since its publication, there have been a large number of changes: 1. The basic detection and estimation theory has remained the same but numerous new results and algorithms have been obtained.
/5(8). This is a book about some of the theory of nonparametric function estimation. The premise is that much insight can be gained even if attention is conﬁned to a Gaussian sequence model y iD iC z i; i2I; () where Iis ﬁnite or countable, f igis ﬁxed and unknown, fz igare i.i.d.
N.0;1/noise vari-ables and is a known noise Size: 2MB. Detection and Estimation in non Gaussian Noise. present several failures when the background scatterers are heterogeneous and non Gaussian, which is the case for ground or sea clutter Author: Frederic Pascal.
On MIMO detection under non-Gaussian target scattering Article (PDF Available) in IEEE Transactions on Information Theory 56(11) - December with 57 Reads How we measure 'reads'.
HIGHER CRITICISM STATISTIC: THEORY AND APPLICATIONS IN NON-GAUSSIAN DETECTION J. JIN Statistics Department, Purdue University, N. University Street, West Lafayette, INUSA E-mail: [email protected] Higher Criticism is a statistic recently proposed by Donoho and Jin5.
It has been shown to be eﬀective in resolving aFile Size: KB. Mixture Model (GMM), a non-Gaussian distribution or a mixture of non-Gaussian dis-tributions does not have an analytically tractable solution, in general. In this dissertation, we study several estimation methods for the non-Gaussian distributions.
For the Maxi-mum Likelihood (ML) estimation, a numerical method is utilized to search for the optimal. respiratory signal with a non-gaussian colored noise. This task, requiring multivariate probability density computa-tions for the data likelihood term, often faces with the lack of analytical multidimensional expressions in the non-gaussian case.
Thus, multidimensional Gaussian distribu-tion is usually used for its simplicity, even if Gaussian. To investigate the non-Gaussian updating a bit closer we ran the same experiment with a decreased assimilation frequency: data is assimilated only every 10 days, starting at day This results in a longer non-linear mixing between the updates and thus should create pdfs which are more non-Gaussian.
The rest of the experiment stays the by: In mathematics, Gaussian measure is a Borel measure on finite-dimensional Euclidean space R n, closely related to the normal distribution in is also a generalization to infinite-dimensional spaces. Gaussian measures are named after the German mathematician Carl Friedrich reason why Gaussian measures are so ubiquitous in probability theory is the central limit theorem.
Guo, D, Shamai, S & Verdú, SEstimation of non-Gaussian random variables in Gaussian noise: Properties of the MMSE. in Proceedings - IEEE International Symposium on Information Theory, ISIT, IEEE International Symposium on Information Theory - Proceedings, pp.
IEEE International Symposium on Information Theory, ISITToronto, ON, Canada, Cited by: We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc., are by: H.
Asadi and B. Seyfe, Source Number Estimation via Entropy Estimation of Eigenvalues (EEE) in Gaussian and Non-Gaussian Noise 1 Abstract— In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of the sources in Gaussian and Non-Gaussian noise.
In this method, the. The book An Introduction to Sparse Stochastic Processes by Unser and Tafti is the first work to systematically build a coherent framework for non-Gaussian processes with sparse representations by wavelets.
Traditional concepts such as Karhunen-Loève analysis of Gaussian processes are nicely complemented by the wavelet analysis of Levy Cited by: model, we derive an approximation of the marginal density for non-Gaussian compo-nents where the dependency structure is explicitly parametrized like a precision matrix.
Section 3 deals with estimation of the model, presenting a powerful algorithm for iden-tifying the non-Gaussian components and their dependency structure. In Section 4, we. Communications in Statistics - Theory and Methods() New non-parametric tests for independence.
Journal of Statistical Computation and SimulationCited by: For estimation this is done in terms of the Cramér-Rao lower bound, and for change detection in terms of the asymptotic properties of the generalized likelihood ration test.
A result of having information intensive products is that they tend to be sensitive to. 2 Detecting Faint Non-Gaussian Signals Superposed on a Gaus-sian Signal The superposition of a non-Gaussian signal with a Gaussian signal can be modeled as Y = N+G, where Y is the observed image, N is the non-Gaussian component and G is the Gaussian component.
We are interested in using transform coeﬃcients to test whether N ≡ 0 or not. Self-similarity parameter estimation and reproduction property for non-Gaussian Hermite processes Alexandra Chronopoulou1 Ciprian A. Tudor2 Frederi G. Viens1; 1 Department of Statistics, Purdue University, N.
University St., West Lafayette, INUSA. This paper provides such a result for linear non-Gaussian systems. It is first shown how a batch of data from a linear state-space model with additive faults and non-Gaussian noise can be transformed to a residual described by a general linear non-Gaussian model.
This also involves a. Summary. Least squares (LS) estimation of model parameters is widely used in geophysics. If the data errors are Gaussian and independent the LS estimators will be maximum likelihood (ML) estimators and will be unbiased and of minimum by: Estimation of linear non-Gaussian acyclic models for latent factors Shohei Shimizua Patrik O.
Hoyerb Aapo Hyv¨arinenb,c aThe Institute of Scientiﬁc and Industrial Research, Osaka University MihogaokaIbaraki, OsakaJAPAN bDept. of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, FIN, Finland.
Likelihood analysis of non-Gaussian measurement time series BY NEIL SHEPHARD Nuffield College, Oxford, 0X1 INF, U.K. e-mail: [email protected] AND MICHAEL K. PITT Department of Statistics, University of Oxford, 0X1 3TG, U.K. e-mail: @ SUMMARY In this paper we provide methods for estimating non-Gaussian time.
Not Gaussian Definition from Wiktionary, the free dictionary. Non-Gaussian stable models do not possess such limitations.
They all share a familiar feature which differentiates them from the Gaussian ones. Their marginal distributions possess heavy "probability tails", always with infinite variance and in some cases with infinite first by: complex nonlinear and non-Gaussian estimation problems to be solved efficiently in an online manner.
The experimental results on comparison with Kalman filtering show the efficacy of the proposed method through illustrative examples. Index Terms: Non-linear System, Kalman Filter, Bayesian Filter, Sequential Estimation, Particle Filter 1.
2 The Bayesian Linear Model Throughout we shall consider the following model, y = Ax+” (1) where A 2 Rm£n, x» p(x) = Q i p(xi), and ”» N(0;Σ”), with x and ” independent.
The important thing to note for our purposes is that the xi are non-Gaussian. We consider two types of variational representation of the non-Gaussian priorsp(xi), which we shall call convex type and integral by: Foundations of Image Science presents a comprehensive treatment of the principles, mathematics, and statistics needed to understand and evaluate imaging systems.
The book is the first to provide a thorough treatment of the continuous-to-discrete, or CD, model of digital : NOOK Book (Ebook). Jeongeun Kim. Parameter Estimation in Stochastic Volatility Models with Missing Data Using Particle Methods and the EM Algorithm.
PhD thesis, University of Pittsburgh, Genshiro Kitagawa. Monte Carlo Filter an Smoother for Non-Gaussian Non-linear State Space Models. This paper studies the problem of sinusoidal frequency estimation in colored non-Gaussian ARMA noises.
A new adaptive approach is proposed by using the second-and third-order statistics of the measurements. Because of the simultaneous establishment of the signal and noise models, the new approach is applicable for tracking the frequencies at each time instant for stationary Cited by: 2.
A General Linear Non-Gaussian State-Space Model identi able (Davies,); in fact, the distributions of y itcould not be uniquely determined, even with a given mean and variance. For generality, we would like to keep the exibility of the SSM model (i.e., do not use speci c structural constraints on A and BAuthor: Kun Zhang, Aapo Hyvärinen.
Geesey and T. Kailath. Applications of the canonical representation to estimation and detection in colored noise. pages Polytechnic Institute of Brooklyn Press, T. Kailath. The innovations approach to detection and estimation theory.
IEEE Proc., 58(5), May T. Kailath. Likelihood ratios for Gaussian processes. By Henrique Helfer. Motivation. Until very recently, only a very limited classes of feasible non Gaussian time series models were available.
For example, one could use extensions of state space models to non Gaussian environments (see, for example, Durbin and Koopman ()), but extensive Monte Carlo simulation is required to numerically evaluate the conditional densities that define the. The estimation of normalizing constants for a family of distributions is a recurrent theme in computational statistics.
After a brief description of a number of applications of interest, including likelihood calculation in a genetic linkage problem, I will proceed to examine two different: bridge sampling (Meng and Wong, ) and maximum profile likelihood (inverse logistic regression, Geyer.
mclust is a popular R package for model-based clustering, classification, and density estimation based on finite Gaussian mixture modelling. An integrated approach to finite mixture models is provided, with functions that combine model-based hierarchical clustering, EM for mixture estimation and several tools for model by: State Estimation and Smoothing for the Probability Hypothesis Density Filter by Sergio I.
Hernandez A thesis submitted to the Victoria University of Wellington in fulﬁlment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model Based Weighted Kernel Independent Component Analysis Lianfang Cai, Xuemin Tian, and Sheng Chen, Fellow, IEEE Abstract—Kernel independent component analysis (KICA) is widely regarded as an effective approach for nonlinear and non-Gaussian process monitoring.
distributed kernel density estimation algorithm and analyze the convergence and consistency of the estimation process. Furthermore, we extend our algorithm to distributed sys-tems under communication and storage constraints by in-troducing a fast and efﬁcient data reduction algorithm.
Ex-periments show that our algorithm can estimate underlyingCited by: In this paper, the fault detection in uncertain multivariate nonlinear non-Gaussian stochastic systems is further investigated. Entropy is introduced to characterize the stochastic behavior of the detection errors, and the entropy optimization principle is established for the fault detection problem.
The principle is to maximize the entropies of the stochastic detection errors in the presence Cited by: 7. Evaluation of Gaussian Processes and other Methods for Non-Linear Regression Carl Edward Rasmussen A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy, Graduate Department of Computer Science, The framework allows for estimation ofCited by: This course covers the two basic approaches to statistical signal processing: estimation and detection.
In estimation, we want to determine a signal’s waveform or some signal aspect(s). Typically the parameter or signal we want is buried in noise.
Estimation theory shows how to. Measures derived for normal distributions are invariant only to linear, nonsingular transformations.
The predictability of nonlinear or non‐Gaussian systems can differ dramatically from that of linear or Gaussian systems [Smith, ]. Methods for non‐Gaussian Cited by: