Download or read book Contributions to Finite Mixture Models with Applications written by Hok Shing Kwong and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mixture Models and Applications written by Nizar Bouguila and published by Springer. This book was released on 2020-08-30 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting and challenging problems such as parameters estimation, model selection, feature selection, etc. The goal of this book is to summarize the recent advances and modern approaches related to these problems. Each contributor presents novel research, a practical study, or novel applications based on mixture models, or a survey of the literature. Reports advances on classic problems in mixture modeling such as parameter estimation, model selection, and feature selection; Present theoretical and practical developments in mixture-based modeling and their importance in different applications; Discusses perspectives and challenging future works related to mixture modeling.
Download or read book Essays on finite mixture models written by Abram van Dijk and published by Rozenberg Publishers. This book was released on 2009 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finite mixture distributions are a weighted average of a finite number of distributions. The latter are usually called the mixture components. The weights are usually described by a multinomial distribution and are sometimes called mixing proportions. The mixture components may be the same type of distributions with di®erent parameter values but they may also be completely different distributions. Therefore, finite mixture distributions are very °exible for modeling data. They are frequently used as a building block within many modern econometric models. The specification of the mixture distribution depends on the modeling problem at hand. In this thesis, we introduce new applications of finite mixtures to deal with several di®erent modeling issues. Each chapter of the thesis focusses on a specific modeling issue. The parameters of some of the resulting models can be estimated using standard techniques but for some of the chapters we need to develop new estimation and inference methods. To illustrate how the methods can be applied, we analyze at least one empirical data set for each approach. These data sets cover a wide range of research fields, such as macroeconomics, marketing, and political science. We show the usefulness of the methods and, in some cases, the improvement over previous methods in the literature.
Download or read book Some Theoretical Contributions to the Evaluation and Assessment of Finite Mixture Models with Applications written by Volodymyr Melnykov and published by . This book was released on 2009 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Finite Mixture Models written by José Manuel Gonçalves Dias and published by . This book was released on 2004 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Mixture Analysis written by Sylvia Fruhwirth-Schnatter and published by CRC Press. This book was released on 2019-01-04 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
Download or read book The Fitting of Finite Mixture Models to Grouped Data written by Peter Noel Jones and published by . This book was released on 1990 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Inference Under Mixture Models written by Jiahua Chen and published by Springer Nature. This book was released on 2023-12-24 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book puts its weight on theoretical issues related to finite mixture models. It shows that a good applicant, is an applicant who understands the issues behind each statistical method. This book is intended for applicants whose interests include some understanding of the procedures they are using, while they do not have to read the technical derivations. At the same time, many researchers find most theories and techniques necessary for the development of various statistical methods, without chasing after one set of research papers, after another. Even though the book emphasizes the theory, it provides accessible numerical tools for data analysis. Readers with strength in developing statistical software, may find it useful.
Download or read book Mixtures written by Kerrie L. Mengersen and published by John Wiley & Sons. This book was released on 2011-05-03 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.
Download or read book Recent Advances in Linear Models and Related Areas written by Shalabh and published by Springer Science & Business Media. This book was released on 2008-07-11 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection contains invited papers by distinguished statisticians to honour and acknowledge the contributions of Professor Dr. Dr. Helge Toutenburg to Statistics on the occasion of his sixty-?fth birthday. These papers present the most recent developments in the area of the linear model and its related topics. Helge Toutenburg is an established statistician and currently a Professor in the Department of Statistics at the University of Munich (Germany) and Guest Professor at the University of Basel (Switzerland). He studied Mathematics in his early years at Berlin and specialized in Statistics. Later he completed his dissertation (Dr. rer. nat. ) in 1969 on optimal prediction procedures at the University of Berlin and completed the post-doctoral thesis in 1989 at the University of Dortmund on the topic of mean squared error superiority. He taught at the Universities of Berlin, Dortmund and Regensburg before joining the University of Munich in 1991. He has various areas of interest in which he has authored and co-authored over 130 research articles and 17 books. He has made pioneering contributions in several areas of statistics, including linear inference, linear models, regression analysis, quality engineering, Taguchi methods, analysis of variance, design of experiments, and statistics in medicine and dentistry.
Download or read book Finite Mixture Models written by Geoffrey McLachlan and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Download or read book Estimation of Finite Mixture Models written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A recorded signal frequently results from the mixture of many signals from several classifiable sources. Knowledge of the contribution of the underlying sources to the recorded signal is valuable in several applications, such as remote sensing. Such mixtures may be analyzed using finite mixture models. Historically, finite mixture models decompose a density as the sum of a finite number of component densities. Current methods for estimating the contribution of each component assume a parametric form for the mixture components. Furthermore, these methods assume a collection of samples from the mixture are observed rather than an aggregate representation of the samples, such as a histogram. This work introduces a method to address the many practical cases where parametric mixture models are insufficient to describe the mixture components. The observed mixture is assumed to occur in an aggregate representation of samples. Thus, the mixture components are represented as finite-length signals or vectors. The proposed method incorporates the first and second order statistics of the mixture components obtained from previously collected samples of the mixture components. The new method is based on the set theoretic method of successive projections onto convex sets (POCS). The set theoretic approach defines a set of feasible solutions as the intersection of sets consistent with the prior knowledge of a desirable solution. POCS is an iterative procedure used to find a point in the set of feasible solutions. This work considers several sets describing the finite mixture model, including a new model set generalizing a set based on the error-in-variables model. To illustrate the viability of the new method, comparisons are made with the expectation-maximization (EM) algorithm for mixtures with parametric components. Simulations of mixture with nonparametric components emphasize the advantages of the new method, since no other methods address mixtures with nonparametric component.
Download or read book Development of Finite Mixture Models to Assess the Adequacy of Non identical Multiple Tooled Manufacturing Processes written by Allen Thomas Bracken and published by . This book was released on 2005 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Global Optimization of Finite Mixture Models written by Jeffrey W. Heath and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Finite Bivariate and Multivariate Beta Mixture Models Learning and Applications written by Narges Manouchehri and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Finite mixture models have been revealed to provide flexibility for data clustering. They have demonstrated high competence and potential to capture hidden structure in data. Modern technological progresses, growing volumes and varieties of generated data, revolutionized computers and other related factors are contributing to produce large scale data. This fact enhances the significance of finding reliable and adaptable models which can analyze bigger, more complex data to identify latent patterns, deliver faster and more accurate results and make decisions with minimal human interaction. Adopting the finest and most accurate distribution that appropriately represents the mixture components is critical. The most widely adopted generative model has been the Gaussian mixture. In numerous real-world applications, however, when the nature and structure of data are non-Gaussian, this modelling fails. One of the other crucial issues when using mixtures is determination of the model complexity or number of mixture components. Minimum message length (MML) is one of the main techniques in frequentist frameworks to tackle this challenging issue. In this work, we have designed and implemented a finite mixture model, using the bivariate and multivariate Beta distributions for cluster analysis and demonstrated its flexibility in describing the intrinsic characteristics of the observed data. In addition, we have applied our estimation and model selection algorithms to synthetic and real datasets. Most importantly, we considered interesting applications such as in image segmentation, software modules defect prediction, spam detection and occupancy estimation in smart buildings.
Download or read book A Study on Variational Component Splitting Approach for Mixture Models written by Kamal Maanicshah Mathin Henry and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increase in use of mobile devices and the introduction of cloud-based services have resulted in the generation of enormous amount of data every day. This calls for the need to group these data appropriately into proper categories. Various clustering techniques have been introduced over the years to learn the patterns in data that might better facilitate the classification process. Finite mixture model is one of the crucial methods used for this task. The basic idea of mixture models is to fit the data at hand to an appropriate distribution. The design of mixture models hence involves finding the appropriate parameters of the distribution and estimating the number of clusters in the data. We use a variational component splitting framework to do this which could simultaneously learn the parameters of the model and estimate the number of components in the model. The variational algorithm helps to overcome the computational complexity of purely Bayesian approaches and the over fitting problems experienced with Maximum Likelihood approaches guaranteeing convergence. The choice of distribution remains the core concern of mixture models in recent research. The efficiency of Dirichlet family of distributions for this purpose has been proved in latest studies especially for non-Gaussian data. This led us to study the impact of variational component splitting approach on mixture models based on several distributions. Hence, our contribution is the application of variational component splitting approach to design finite mixture models based on inverted Dirichlet, generalized inverted Dirichlet and inverted Beta-Liouville distributions. In addition, we also incorporate a simultaneous feature selection approach for generalized inverted Dirichlet mixture model along with component splitting as another experimental contribution. We evaluate the performance of our models with various real-life applications such as object, scene, texture, speech and video categorization.
Download or read book Computational Methods in Finite Mixtures Using Approximate Information and Regression Linked to the Mixture Mean written by Andrew Martin Raim and published by . This book was released on 2014 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finite mixture distributions are used in applications because of their ability to support heterogeneity. They also present interesting analytical challenges, often requiring special consideration in the selection of an appropriate model, inference of unknown parameters, and identifiability. The main contributions of this thesis are providing an approximation to the information matrix of a finite mixture of an arbitrary member of the exponential family, and a novel extension of the generalized linear model (GLM) with an underlying finite mixture distribution.
