Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Application Of The Maximum Likelihood Ml Principle And Expectation Maximization Em Technique To Estimation Of Affine Modeled Image Motion
Download Application Of The Maximum Likelihood Ml Principle And Expectation Maximization Em Technique To Estimation Of Affine Modeled Image Motion full books in PDF, epub, and Kindle. Read online Application Of The Maximum Likelihood Ml Principle And Expectation Maximization Em Technique To Estimation Of Affine Modeled Image Motion ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Application of the Maximum Likelihood (ML) Principle and Expectation-maximization (EM) Technique to Estimation of Affine Modeled Image Motion by : Samir Jasir Shaltaf
Download or read book Application of the Maximum Likelihood (ML) Principle and Expectation-maximization (EM) Technique to Estimation of Affine Modeled Image Motion written by Samir Jasir Shaltaf and published by . This book was released on 1994 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of the Maximum Likelihood (ML) Principle and Expectation-maximization (EM) Technique to Estimation of Affine Modeled Image Motion by : Samir J. Shaltaf
Download or read book Application of the Maximum Likelihood (ML) Principle and Expectation-maximization (EM) Technique to Estimation of Affine Modeled Image Motion written by Samir J. Shaltaf and published by . This book was released on 1992 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation by : Scott R. Eliason
Download or read book Maximum Likelihood Estimation written by Scott R. Eliason and published by SAGE. This book was released on 1993 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a short introduction to Maximum Likelihood (ML) Estimation. It provides a general modeling framework that utilizes the tools of ML methods to outline a flexible modeling strategy that accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models linking endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, the author discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1993-07 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Expectation Maximization and Its Application in Modeling, Segmentation and Anomaly Detection by : Ritesh Ganju
Download or read book Expectation Maximization and Its Application in Modeling, Segmentation and Anomaly Detection written by Ritesh Ganju and published by . This book was released on 2006 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Expectation Maximization (EM) is a general purpose algorithm for solving maximum likelihood estimation problems in a wide variety of situations best described as incomplete data problems. The incompleteness of the data may arise due to missing data, truncated distributions, etc. One such case is a mixture model, where the class association of the data is unknown. In these models, the EM algorithm is used to estimate the parameters of parametric mixture distributions along with the probabilities of occurrence. In this thesis, the EM algorithm is employed to estimate different mixture models for raw single and multi-band electro-optical Infra Red (IF) data"--Abstract, leaf iii.
Book Synopsis Maximum-Likelihood Deconvolution by : Jerry M. Mendel
Download or read book Maximum-Likelihood Deconvolution written by Jerry M. Mendel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
Book Synopsis Some Applications of Expectation Maximization Algorithm by : Loc Nguyen
Download or read book Some Applications of Expectation Maximization Algorithm written by Loc Nguyen and published by Eliva Press. This book was released on 2022-03-25 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expectation maximization (EM) algorithm is a popular and powerful mathematical method for statistical parameter estimation in case that there exist both observed data and hidden data. This book focuses on applications of EM in which the implicit relationship is essential to connect observed data and hidden data. In other words, such applications reinforce EM which in turn extends estimation methods like maximum likelihood estimation (MLE) or moment method.
Book Synopsis Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm by : Antara Majumdar
Download or read book Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm written by Antara Majumdar and published by . This book was released on 2007 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Likelihood based estimation of stochastic models when one of the explanatory variables is masked by measurement error, is presented. Special methods are required to estimate the parameters of a model with one or more explanatory variables that are measured with error. In such models, the variable measured with error is unobservable. Only an unbiased manifestation is observable. The method proposed, provides an adjustment to obtain unbiased estimates of model parameters. The correction of bias, however, is not possible without additional identifying information. An instrumental variable is a practical form of additional information that can be used for this purpose. By treating the unobservable explanatory variable as 'missing' data the Markov Chain Monte Carlo Expectation Maximization (MCEM) algorithm is applied for maximum likelihood estimation of the parameters of a measurement error model with identifying information in the form of an instrumental variable. Implementation strategies, computational aspects, behavior of the estimators and inference resulting from application of the MCEM algorithm to the instrumental variable measurement error model are studied. A general methodology is developed that encompasses a variety of previously studied special case models and it is shown how they all can be modeled and estimated using the MCEM algorithm. Through our method it is shown how a structural logistic regression measurement error model can be directly fitted without the probit approximation. This was not possible prior to the research presented in this dissertation. The proposed methodology is compared numerically with the exact maximum likelihood estimates for two normal family models. Also, the behavior of the method is investigated when one of the variance parameters is near the boundary of the parameter space. The problem of measurement error in a survival time model with right censoring is considered and it is shown how the proposed method can be used to estimate a hazard function model, by construction of some special likelihoods and further methodological development. Two methods have been proposed, one of which is a semi-parametric method and the other is full parametric.
Book Synopsis Maximum Likelihood Sequence Estimation Via the Expectation Maximization Algorithm in the Presence of Random Phase and Amplitude Fading by : Jae Choong Han
Download or read book Maximum Likelihood Sequence Estimation Via the Expectation Maximization Algorithm in the Presence of Random Phase and Amplitude Fading written by Jae Choong Han and published by . This book was released on 1994 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Penalized Likelihood Estimation by : P.P.B. Eggermont
Download or read book Maximum Penalized Likelihood Estimation written by P.P.B. Eggermont and published by Springer Nature. This book was released on 2020-12-15 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.
Book Synopsis Use of the Generalized Maximum Likelihood (gml) Algorithm for Estimation of Markovian Modelled Image Motion by : David W. Foxall
Download or read book Use of the Generalized Maximum Likelihood (gml) Algorithm for Estimation of Markovian Modelled Image Motion written by David W. Foxall and published by . This book was released on 1990 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Image Models (and their Speech Model Cousins) by : Stephen Levinson
Download or read book Image Models (and their Speech Model Cousins) written by Stephen Levinson and published by Springer Science & Business Media. This book was released on 1996-08-29 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores the interface between two diverse areas of applied mathematics which are both 'customers' of the maximum likelihood methodology; emission tomography and hidden Markov models as an approach to speech understanding. Other areas where maximum likelihood is used in this volume include parsing of text (Jelinek), microstructure of materials (Ji), DNA sequencing (Nelson). Most of the participants were in the main areas of speech or emission density reconstruction.
Book Synopsis Machine Learning for Vision-Based Motion Analysis by : Liang Wang
Download or read book Machine Learning for Vision-Based Motion Analysis written by Liang Wang and published by Springer. This book was released on 2010-11-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
Book Synopsis Estimation and Feature Selection in High-dimensional Mixtures-of-experts Models by : Bao Tuyen Huynh
Download or read book Estimation and Feature Selection in High-dimensional Mixtures-of-experts Models written by Bao Tuyen Huynh and published by . This book was released on 2019 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis deals with the problem of modeling and estimation of high-dimensional MoE models, towards effective density estimation, prediction and clustering of such heterogeneous and high-dimensional data. We propose new strategies based on regularized maximum-likelihood estimation (MLE) of MoE models to overcome the limitations of standard methods, including MLE estimation with Expectation-Maximization (EM) algorithms, and to simultaneously perform feature selection so that sparse models are encouraged in such a high-dimensional setting. We first introduce a mixture-of-experts' parameter estimation and variable selection methodology, based on l1 (lasso) regularizations and the EM framework, for regression and clustering suited to high-dimensional contexts. Then, we extend the method to regularized mixture of experts models for discrete data, including classification. We develop efficient algorithms to maximize the proposed l1 -penalized observed-data log-likelihood function. Our proposed strategies enjoy the efficient monotone maximization of the optimized criterion, and unlike previous approaches, they do not rely on approximations on the penalty functions, avoid matrix inversion, and exploit the efficiency of the coordinate ascent algorithm, particularly within the proximal Newton-based approach.
Book Synopsis Maximum Likelihood Estimation by : William H. Greene
Download or read book Maximum Likelihood Estimation written by William H. Greene and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maximum likelihood (ML) estimation is the foundational platform for modern empirical research. The methodology provides organizing principles for combining observational information and underlying theory to understand the workings of the natural and social environment in the face of uncertainty about the origins and interrelations of those data. Alternatives to ML estimator (MLE) are proposed in comparison to or as modifications of the central methodology. This entry develops the topic of ML estimation from the viewpoints of classical statistics and modern econometrics. It begins with an understanding of the methodology. This departs from a consideration of what is meant by the likelihood function and a useful description of the notion of estimation based on the principle of ML. It then develops the theory of the MLE. The MLE has a set of properties, including consistency and efficiency, which establish it among classes of estimators. These are the basic results that motivate MLE as a method of estimation. This entry examines the topics of inference and hypothesis testing in the ML framework - how to compute standard errors and how to accommodate sampling variability in estimation and testing. It concludes with modern extensions of ML that broaden the framework. Notions of robust estimation and inference, latent heterogeneity in panel data and quasi-ML are also considered. Some practical aspects of ML estimation, such as optimization and maximum simulated likelihood are considered in passing. Examples are woven through the development. This entry introduces the theory, language, and practicalities of the methodology.
Book Synopsis Generalized Principal Component Analysis by : René Vidal
Download or read book Generalized Principal Component Analysis written by René Vidal and published by Springer. This book was released on 2016-04-11 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. René Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.
Book Synopsis Maximum-likelihood Deconvolution by : Jerry M. Mendel
Download or read book Maximum-likelihood Deconvolution written by Jerry M. Mendel and published by . This book was released on 1990-01-01 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt:
