Download or read book Statistics Based on Dirichlet Processes and Related Topics written by Hajime Yamato and published by Springer Nature. This book was released on 2020-07-17 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the properties associated with the Dirichlet process, describing its use a priori for nonparametric inference and the Bayes estimate to obtain limits for the estimable parameter. It presents the limits and the well-known U- and V-statistics as a convex combination of U-statistics, and by investigating this convex combination, it demonstrates these three statistics. Next, the book notes that the Dirichlet process gives the discrete distribution with probability one, even if the parameter of the process is continuous. Therefore, there are duplications among the sample from the distribution, which are discussed. Because sampling from the Dirichlet process is described sequentially, it can be described equivalently by the Chinese restaurant process. Using this process, the Donnelly–Tavaré–Griffiths formulas I and II are obtained, both of which give the Ewens’ sampling formula. The book then shows the convergence and approximation of the distribution for its number of distinct components. Lastly, it explains the interesting properties of the Griffiths–Engen–McCloskey distribution, which is related to the Dirichlet process and the Ewens’ sampling formula.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book Partitions Hypergeometric Systems and Dirichlet Processes in Statistics written by Shuhei Mano and published by Springer. This book was released on 2018-07-12 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on statistical inferences related to various combinatorial stochastic processes. Specifically, it discusses the intersection of three subjects that are generally studied independently of each other: partitions, hypergeometric systems, and Dirichlet processes. The Gibbs partition is a family of measures on integer partition, and several prior processes, such as the Dirichlet process, naturally appear in connection with infinite exchangeable Gibbs partitions. Examples include the distribution on a contingency table with fixed marginal sums and the conditional distribution of Gibbs partition given the length. The A-hypergeometric distribution is a class of discrete exponential families and appears as the conditional distribution of a multinomial sample from log-affine models. The normalizing constant is the A-hypergeometric polynomial, which is a solution of a system of linear differential equations of multiple variables determined by a matrix A, called A-hypergeometric system. The book presents inference methods based on the algebraic nature of the A-hypergeometric system, and introduces the holonomic gradient methods, which numerically solve holonomic systems without combinatorial enumeration, to compute the normalizing constant. Furher, it discusses Markov chain Monte Carlo and direct samplers from A-hypergeometric distribution, as well as the maximum likelihood estimation of the A-hypergeometric distribution of two-row matrix using properties of polytopes and information geometry. The topics discussed are simple problems, but the interdisciplinary approach of this book appeals to a wide audience with an interest in statistical inference on combinatorial stochastic processes, including statisticians who are developing statistical theories and methodologies, mathematicians wanting to discover applications of their theoretical results, and researchers working in various fields of data sciences.
Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Download or read book The Poisson Dirichlet Distribution and Related Topics written by Shui Feng and published by Springer Science & Business Media. This book was released on 2010-05-27 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting a comprehensive study of the Poisson-Dirichlet distribution, this volume emphasizes recent progress in evolutionary dynamics and asymptotic behaviors. The self-contained text presents methods and techniques that appeal to researchers in a wide variety of subjects.
Download or read book Combinatorial Stochastic Processes written by Jim Pitman and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this text is to bring graduate students specializing in probability theory to current research topics at the interface of combinatorics and stochastic processes. There is particular focus on the theory of random combinatorial structures such as partitions, permutations, trees, forests, and mappings, and connections between the asymptotic theory of enumeration of such structures and the theory of stochastic processes like Brownian motion and Poisson processes.
Download or read book Practical Nonparametric and Semiparametric Bayesian Statistics written by Dipak D. Dey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Download or read book Conditionally Dependent Dirichlet Processes for Modelling Naturally Correlated Data Sources written by Dinh Phung and published by . This book was released on 2012 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: "We introduce a new class of conditionally dependent Dirichlet processes (CDP) for hierarchical mixture modelling of naturally correlated data sources. This class of models provides a Bayesian nonparametric approach for modelling a range of challenging datasets which typically consists of heterogeneous observations from multiple correlated data channels. Some typical examples include annotated social media, networks in community where information about friendship and relation coexist with user's pro les, medical records where patient's information exists in several dimension (demographic information, medical history, drug uses and so on). The proposed framework can easily be tailored to model multiple data sources which are correlated by some latent underlying processes, whereas most of existing topic models, notably hierarchical Dirichlet processes (HDP), is designed for only a single data observation channel. In these existing approaches, data are grouped into documents (e.g., text documents or they are grouped according to some covariates such as time or location). Our approach is di erent: we view context as distributions over some index space and model both topics and contexts jointly. Distributions over topic parameters are modelled according to the usual Dirichlet processes. Stick-breaking representation gives rise to explicit realizations of topic atoms which we use as an indexing mechanism to induce conditional random mixture distributions on the context observation spaces { loosely speaking, we use a stochastic process, being DP, to conditionally `index' other stochastic processes. The later can be designed on any suitable family of stochastic processes to suit modelling needs or data types of contexts (such as Beta or Gaussian processes). Dirichlet process is of course an obvious choice. Our model can be viewed as an integration of the hierarchical Dirichlet process (HDP) and the recent nested Dirichlet process (nDP) with shared mixture components. In fact, it provides an interesting interpretation whereas, under a suitable parameterization, integrating out the topic components results in a nested DP, whereas integrating out the context components results in a hierarchical DP. Di erent approaches for posterior inference exist. This paper focus on the development of an auxiliary conditional Gibbs sampling in which both topic and context atoms are marginalized out. We demonstrate the framework on synthesis datasets for temporal topic modelling and trajectory discovery in videos surveillance. We then demonstrate an application on a current visual category classi cation challenge in computer vision for which we signi cantly outperform the current reported state-of-the-art results. Finally, it is worthwide to note that our proposed approach can be easily twisted to accommodate di erent forms of supervision (weakly annotated data, semi-supervision) and to perform prediction." -- Abstract.
Download or read book Current Issues in Statistical Inference written by Dev Basu and published by IMS. This book was released on 1992 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Theory and Use of the EM Algorithm written by Maya R. Gupta and published by Now Publishers Inc. This book was released on 2011 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use.
Download or read book Dirichlet and Related Distributions written by Kai Wang Ng and published by John Wiley & Sons. This book was released on 2011-05-03 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Dirichlet distribution appears in many areas of application, which include modelling of compositional data, Bayesian analysis, statistical genetics, and nonparametric inference. This book provides a comprehensive review of the Dirichlet distribution and two extended versions, the Grouped Dirichlet Distribution (GDD) and the Nested Dirichlet Distribution (NDD), arising from likelihood and Bayesian analysis of incomplete categorical data and survey data with non-response. The theoretical properties and applications are also reviewed in detail for other related distributions, such as the inverted Dirichlet distribution, Dirichlet-multinomial distribution, the truncated Dirichlet distribution, the generalized Dirichlet distribution, Hyper-Dirichlet distribution, scaled Dirichlet distribution, mixed Dirichlet distribution, Liouville distribution, and the generalized Liouville distribution. Key Features: Presents many of the results and applications that are scattered throughout the literature in one single volume. Looks at the most recent results such as survival function and characteristic function for the uniform distributions over the hyper-plane and simplex; distribution for linear function of Dirichlet components; estimation via the expectation-maximization gradient algorithm and application; etc. Likelihood and Bayesian analyses of incomplete categorical data by using GDD, NDD, and the generalized Dirichlet distribution are illustrated in detail through the EM algorithm and data augmentation structure. Presents a systematic exposition of the Dirichlet-multinomial distribution for multinomial data with extra variation which cannot be handled by the multinomial distribution. S-plus/R codes are featured along with practical examples illustrating the methods. Practitioners and researchers working in areas such as medical science, biological science and social science will benefit from this book.
Download or read book Statistical Sciences written by Volker Mammitzsch and published by Walter de Gruyter. This book was released on 2011-05-09 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series is aimed specifically at publishing peer reviewed reviews and contributions presented at workshops and conferences. Each volume is associated with a particular conference, symposium or workshop. These events cover various topics within pure and applied mathematics and provide up-to-date coverage of new developments, methods and applications.
Download or read book Perspectives In Mathematical Science I Probability And Statistics written by N S Narasimha Sastry and published by World Scientific. This book was released on 2009-07-06 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a collection of invited articles by distinguished probabilists and statisticians on the occasion of the Platinum Jubilee Celebrations of the Indian Statistical Institute — a notable institute with significant achievement in research areas of statistics, probability and mathematics — in 2007.With a wide coverage of topics in probability and statistics, the articles provide a current perspective of different areas of research, emphasizing the major challenging issues. The book also proves its reference and utility value for practitioners as the articles in Statistics contain applications of the methodology that will be of use to practitioners. To professional statisticians and mathematicians, this is a unique volume for its illuminating perspectives on several important aspects of probability and statistics.
Download or read book Survival Analysis written by John Crowley and published by IMS. This book was released on 1982 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances In Statistical Modeling And Inference Essays In Honor Of Kjell A Doksum written by Vijay Nair and published by World Scientific. This book was released on 2007-03-15 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics.This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.
Download or read book Applications of Topic Models written by Jordan Boyd-Graber and published by Now Publishers. This book was released on 2017-07-13 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models.
Download or read book Introduction to the Theory of Non Symmetric Dirichlet Forms written by Zhi-Ming Ma and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to give a streamlined introduction to the theory of (not necessarily symmetric) Dirichlet forms on general state spaces. It includes both the analytic and the probabilistic part of the theory up to and including the construction of an associated Markov process. It is based on recent joint work of S. Albeverio and the two authors and on a one-year-course on Dirichlet forms taught by the second named author at the University of Bonn in 1990/9l. It addresses both researchers and graduate students who require a quick but complete introduction to the theory. Prerequisites are a basic course in probabil ity theory (including elementary martingale theory up to the optional sampling theorem) and a sound knowledge of measure theory (as, for example, to be found in Part I of H. Bauer [B 78]). Furthermore, an elementary course on lin ear operators on Banach and Hilbert spaces (but without spectral theory) and a course on Markov processes would be helpful though most of the material needed is included here.
