Download or read book Mixture of expert models Statistical analysis method written by Jula Kabeto Bunkure and published by GRIN Verlag. This book was released on 2020-06-16 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Document from the year 2020 in the subject Mathematics - Statistics, grade: Book chapter, Bahir Dar University (Ethiopian Institute of Textile and fashion technology), course: Statistical analysis method, language: English, abstract: Mixtures of experts models consist of a set of experts, which model conditional probabilistic processes, and a gate which combines the probabilities of the experts. The probabilistic basis for the mixture of experts is that of a mixture model in which the experts form the input conditional mixture components while the gate outputs form the input conditional mixture weights. A straightforward generalisation of ME models is the hierarchical mixtures of experts (HME) class of models, in which each expert is made up of a mixture of experts in a recursive fashion. This principle states that complex problems can be better solved by decomposing them into smaller tasks. In mixtures of experts the assumption is that there are separate processes in the underlying process of generating the data. Modelling of these processes is performed by the experts while the decision of which process to use is modelled by the gate. Mixtures of experts have many connections with other algorithms such as tree-based methods, mixture models and switching regression. In this, I review the paper by Rasmussen and Ghahramani to see how closely the mixtures of experts model resembles these other algorithms, and what is novel about it. The aim of this review is to adopt the method used in the current article to local precipitation data.
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 489 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 Mixtures written by Kerrie L. Mengersen and published by John Wiley & Sons. This book was released on 2011-05-03 with total page 352 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 The Oxford Handbook of Applied Bayesian Analysis written by Anthony O' Hagan and published by OUP Oxford. This book was released on 2010-03-18 with total page 924 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.
Download or read book Time Series Analysis Methods and Applications written by and published by Elsevier. This book was released on 2012-05-18 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. - Comprehensively presents the various aspects of statistical methodology - Discusses a wide variety of diverse applications and recent developments - Contributors are internationally renowened experts in their respective areas
Download or read book Time Series Analysis Methods and Applications written by Tata Subba Rao and published by Elsevier. This book was released on 2012-06-26 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.
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 Advanced Methods for Modeling Markets written by Peter S. H. Leeflang and published by Springer. This book was released on 2017-08-29 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents advanced techniques to modeling markets, with a wide spectrum of topics, including advanced individual demand models, time series analysis, state space models, spatial models, structural models, mediation, models that specify competition and diffusion models. It is intended as a follow-on and companion to Modeling Markets (2015), in which the authors presented the basics of modeling markets along the classical steps of the model building process: specification, data collection, estimation, validation and implementation. This volume builds on the concepts presented in Modeling Markets with an emphasis on advanced methods that are used to specify, estimate and validate marketing models, including structural equation models, partial least squares, mixture models, and hidden Markov models, as well as generalized methods of moments, Bayesian analysis, non/semi-parametric estimation and endogeneity issues. Specific attention is given to big data. The market environment is changing rapidly and constantly. Models that provide information about the sensitivity of market behavior to marketing activities such as advertising, pricing, promotions and distribution are now routinely used by managers for the identification of changes in marketing programs that can improve brand performance. In today’s environment of information overload, the challenge is to make sense of the data that is being provided globally, in real time, from thousands of sources. Although marketing models are now widely accepted, the quality of the marketing decisions is critically dependent upon the quality of the models on which those decisions are based. This volume provides an authoritative and comprehensive review, with each chapter including: · an introduction to the method/methodology · a numerical example/application in marketing · references to other marketing applications · suggestions about software. Featuring contributions from top authors in the field, this volume will explore current and future aspects of modeling markets, providing relevant and timely research and techniques to scientists, researchers, students, academics and practitioners in marketing, management and economics.
Download or read book Kernel Methods for Pattern Analysis written by John Shawe-Taylor and published by Cambridge University Press. This book was released on 2004-06-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Experiments with Mixtures written by John A. Cornell and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most comprehensive, single-volume guide to conductingexperiments with mixtures "If one is involved, or heavily interested, in experiments onmixtures of ingredients, one must obtain this book. It is, as wasthe first edition, the definitive work." -Short Book Reviews (Publication of the International StatisticalInstitute) "The text contains many examples with worked solutions and with itsextensive coverage of the subject matter will prove invaluable tothose in the industrial and educational sectors whose work involvesthe design and analysis of mixture experiments." -Journal of the Royal Statistical Society "The author has done a great job in presenting the vitalinformation on experiments with mixtures in a lucid and readablestyle. . . . A very informative, interesting, and useful book on animportant statistical topic." -Zentralblatt fur Mathematik und Ihre Grenzgebiete Experiments with Mixtures shows researchers and students how todesign and set up mixture experiments, then analyze the data anddraw inferences from the results. Virtually every technique thathas appeared in the literature of mixtures can be found here, andcomputing formulas for each method are provided with completelyworked examples. Almost all of the numerical examples are takenfrom real experiments. Coverage begins with Scheffe latticedesigns, introducing the use of independent variables, and endswith the most current methods. New material includes: * Multiple response cases * Residuals and least-squares estimates * Categories of components: Mixtures of mixtures * Fixed as well as variable values for the major componentproportions * Leverage and the Hat Matrix * Fitting a slack-variable model * Estimating components of variances in a mixed model using ANOVAtable entries * Clarification of blocking mates and choice of mates * Optimizing several responses simultaneously * Biplots for multiple responses
Download or read book Model Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.
Download or read book Ensemble Learning Algorithms With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2021-04-26 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Download or read book Statistical and Machine Learning Data Mining written by Bruce Ratner and published by CRC Press. This book was released on 2017-07-12 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
Download or read book Applied Bayesian Hierarchical Methods written by Peter D. Congdon and published by CRC Press. This book was released on 2010-05-19 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models involves complex data structures and is often described as a revolutionary development. An intermediate-level treatment of Bayesian hierarchical models and their applications, Applied Bayesian Hierarchical Methods demonstrates the advantages of a Bayesian approach
Download or read book Handbook of Mixed Membership Models and Their Applications written by Edoardo M. Airoldi and published by CRC Press. This book was released on 2014-11-06 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating more than 20 years of the editors' and contributors' statistical work in mixed membership modeling, this handbook shows how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, readers will discover how to characterize complex multivariate data in a range of areas.
Download or read book Encyclopedia of Measurement and Statistics written by Neil J. Salkind and published by SAGE. This book was released on 2007 with total page 1417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description
Download or read book Handbook of Missing Data Methodology written by Geert Molenberghs and published by CRC Press. This book was released on 2014-11-06 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
