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Author : Sanjeena Dang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)
Download or read book Variational Approximations and Other Topics in Mixture Models written by Sanjeena Dang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Geoffrey McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 047165406X
Total Pages : 419 pages
Book Rating : 4.4/5 (716 download)
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.
Author : Nizar Bouguila
Publisher : Springer
ISBN 13 : 3030238768
Total Pages : 355 pages
Book Rating : 4.0/5 (32 download)
Download or read book Mixture Models and Applications written by Nizar Bouguila and published by Springer. This book was released on 2019-08-13 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.
Author : M. Emre Celebi
Publisher : Springer
ISBN 13 : 3319092596
Total Pages : 420 pages
Book Rating : 4.3/5 (19 download)
Download or read book Partitional Clustering Algorithms written by M. Emre Celebi and published by Springer. This book was released on 2014-11-07 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on partitional clustering algorithms, which are commonly used in engineering and computer scientific applications. The goal of this volume is to summarize the state-of-the-art in partitional clustering. The book includes such topics as center-based clustering, competitive learning clustering and density-based clustering. Each chapter is contributed by a leading expert in the field.
Author : Hieu Nguyen Dinh
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (129 download)
Download or read book Variational Approaches For Learning Finite Scaled Dirichlet Mixture Models written by Hieu Nguyen Dinh and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With a massive amount of data created on a daily basis, the ubiquitous demand for data analysis is undisputed. Recent development of technology has made machine learning techniques applicable to various problems. Particularly, we emphasize on cluster analysis, an important aspect of data analysis. Recent works with excellent results on the aforementioned task using finite mixture models have motivated us to further explore their extents with different applications. In other words, the main idea of mixture model is that the observations are generated from a mixture of components, in each of which the probability distribution should provide strong flexibility in order to fit numerous types of data. Indeed, the Dirichlet family of distributions has been known to achieve better clustering performances than those of Gaussian when the data are clearly non-Gaussian, especially proportional data. Thus, we introduce several variational approaches for finite Scaled Dirichlet mixture models. The proposed algorithms guarantee reaching convergence while avoiding the computational complexity of conventional Bayesian inference. In summary, our contributions are threefold. First, we propose a variational Bayesian learning framework for finite Scaled Dirichlet mixture models, in which the parameters and complexity of the models are naturally estimated through the process of minimizing the Kullback-Leibler (KL) divergence between the approximated posterior distribution and the true one. Secondly, we integrate component splitting into the first model, a local model selection scheme, which gradually splits the components based on their mixing weights to obtain the optimal number of components. Finally, an online variational inference framework for finite Scaled Dirichlet mixture models is developed by employing a stochastic approximation method in order to improve the scalability of finite mixture models for handling large scale data in real time. The effectiveness of our models is validated with real-life challenging problems including object, texture, and scene categorization, text-based and image-based spam email detection.
Author : Sylvia Fruhwirth-Schnatter
Publisher : CRC Press
ISBN 13 : 0429508247
Total Pages : 522 pages
Book Rating : 4.4/5 (295 download)
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.
Author : Zeinab Arjmandiasl
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)
Download or read book Variational Learning for Finite Shifted-Scaled Dirichlet Mixture Model and Its Applications written by Zeinab Arjmandiasl and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the huge amount of data produced every day, the interest in data mining and machine learning techniques has been growing. Ongoing advancement of technology has made AI systems subject to different issues. Data clustering is an important aspect of data analysis which is the process of grouping similar observations in the same subset. Among known clustering techniques, finite mixture models have led to outstanding results that created an inspiration toward further exploration of various mixture models and applications. The main idea of this clustering technique is to fit a mixture of components generated from a predetermined probability distribution into the data through parameter approximation of the components. Therefore, choosing a proper distribution based on the type of the data is another crucial step in data analysis. Although the Gaussian distribution has been widely used with mixture models, the Dirichlet family of distributions have been known to achieve better results particularly when dealing with proportional and non-Gaussian data. Another crucial part in statistical modelling is the learning process. Among the conventional estimation approaches, Maximum Likelihood (ML) is widely used due to its simplicity in terms of implementation but it has some drawbacks, too. Bayesian approach has overcome some of the disadvantages of ML approach via taking prior knowledge into account. However, it creates new issues such as need for additional estimation methods due to the intractability of parameters' marginal probabilities. In this thesis, these limitations are discussed and addressed via defining a variational learning framework for finite shifted-scaled Dirichlet mixture model. The motivation behind applying variational inference is that compared to conventional Bayesian approach, it is much less computationally costly. Furthermore, in this method, the optimal number of components is estimated along with the parameter approximation automatically and simultaneously while convergence is guaranteed. The performance of our model, in terms of accuracy of clustering, is validated on real world challenging medical applications, including image processing, namely, Malaria detection, breast cancer diagnosis and cardiovascular disease detection as well as text-based spam email detection. Finally, in order to evaluate the merits of our model effectiveness, it is compared with four other widely used methods.
Author : Edoardo M. Airoldi
Publisher : CRC Press
ISBN 13 : 1466504099
Total Pages : 608 pages
Book Rating : 4.4/5 (665 download)
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.
Author : A. Dahanayake
Publisher : IOS Press
ISBN 13 : 1643680455
Total Pages : 562 pages
Book Rating : 4.6/5 (436 download)
Download or read book Information Modelling and Knowledge Bases XXXI written by A. Dahanayake and published by IOS Press. This book was released on 2020-01-06 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information modeling and knowledge bases have become an important area of academic and industry research in the 21st century, addressing complexities of modeling that reach beyond the traditional borders of information systems and academic computer science research. This book presents 32 reviewed, selected and updated papers delivered at the 29th International Conference on Information Modeling and Knowledge Bases (EJC2019), held in Lappeenranta, Finland, from 3 to 7 June 2019. In addition, two papers based on the keynote presentations and one paper edited from the discussion of the panel session are included in the book. The conference provided a forum to exchange scientific results and experience, and attracted academics and practitioners working with information and knowledge. The papers cover a wide range of topics, ranging from knowledge discovery through conceptual and linguistic modeling, knowledge and information modeling and discovery, cross-cultural communication and social computing, environmental modeling and engineering, and multimedia data modeling and systems to complex scientific problem-solving. The conference presentation sessions: Learning and Linguistics; Systems and Processes; Data and Knowledge Representation; Models and Interface; Formalizations and Reasoning; Models and Modeling; Machine Learning; Models and Programming; Environment and Predictions; and Emotion Modeling and Social Networks reflect the main themes of the conference. The book also includes 2 extended publications of keynote addresses: ‘Philosophical Foundations of Conceptual Modeling’ and ́Sustainable Solid Waste Management using Life Cycle Modeling for Environmental Impact Assessment’, as well as additional material covering the discussion and findings of the panel session. Providing an overview of current research in the field, the book will be of interest to all those working with information systems, information modeling and knowledge bases.
Author : Justin Grimmer
Publisher : Princeton University Press
ISBN 13 : 0691207550
Total Pages : 360 pages
Book Rating : 4.6/5 (912 download)
Download or read book Text as Data written by Justin Grimmer and published by Princeton University Press. This book was released on 2022-03-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
Author : Wim J. van der Linden
Publisher : CRC Press
ISBN 13 : 1315360446
Total Pages : 493 pages
Book Rating : 4.3/5 (153 download)
Download or read book Handbook of Item Response Theory written by Wim J. van der Linden and published by CRC Press. This book was released on 2017-03-31 with total page 493 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.
Author : Adam Krzyzak
Publisher : Springer Nature
ISBN 13 : 3031230280
Total Pages : 336 pages
Book Rating : 4.0/5 (312 download)
Download or read book Structural, Syntactic, and Statistical Pattern Recognition written by Adam Krzyzak and published by Springer Nature. This book was released on 2023-01-01 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more.
Author : Bruce G. Lindsay
Publisher : IMS
ISBN 13 : 9780940600324
Total Pages : 184 pages
Book Rating : 4.6/5 (3 download)
Download or read book Mixture Models written by Bruce G. Lindsay and published by IMS. This book was released on 1995 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Måns Magnusson
Publisher : Linköping University Electronic Press
ISBN 13 : 9176852881
Total Pages : 75 pages
Book Rating : 4.1/5 (768 download)
Download or read book Scalable and Efficient Probabilistic Topic Model Inference for Textual Data written by Måns Magnusson and published by Linköping University Electronic Press. This book was released on 2018-04-27 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.
Author : Folgert Karsdorp
Publisher : Princeton University Press
ISBN 13 : 0691172366
Total Pages : 352 pages
Book Rating : 4.6/5 (911 download)
Download or read book Humanities Data Analysis written by Folgert Karsdorp and published by Princeton University Press. This book was released on 2021-01-12 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations
Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author : M. Tropmann-Frick
Publisher : IOS Press
ISBN 13 : 1643681419
Total Pages : 370 pages
Book Rating : 4.6/5 (436 download)
Download or read book Information Modelling and Knowledge Bases XXXII written by M. Tropmann-Frick and published by IOS Press. This book was released on 2021-01-04 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information modeling and knowledge bases are important technologies for academic and industrial research that goes beyond the traditional borders of information systems and computer science. The amount and complexity of information to be dealt with grows continually, as do the levels of abstraction and the size of databases. This book presents the proceedings of the 30th International Conference on Information Modelling and Knowledge Bases (EJC2020), due to be held in Hamburg, Germany on 8 and 9 June 2020, but instead held as a virtual conference on the same dates due to the Corona-virus pandemic restrictions. The conference provides a research forum for the exchange of scientific results and experiences, and brings together experts from different areas of computer science and other disciplines with a common interest in information modeling and knowledge bases. The subject touches on many disciplines, with philosophy and logic, cognitive science, knowledge management, linguistics and management science, as well as the emerging fields of data science and machine learning, all being relevant areas. The 23 reviewed, selected, and upgraded contributions included here are the result of presentations, comments, and discussions from the conference, and reflect the themes of the conference sessions: learning and linguistics; systems and processes; data and knowledge representation; models and interfaces; formalizations and reasoning; models and modeling; machine learning; models and programming; environment and predictions; modeling emotion; and social networks. The book provides an overview of current research and applications, and will be of interest to all those working in the field.
