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Development Of Markov Random Field Models Based On Exponential Family Conditional Distributions
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Book Synopsis An Introduction to Conditional Random Fields by : Charles Sutton
Download or read book An Introduction to Conditional Random Fields written by Charles Sutton and published by Now Pub. This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. The monograph does not assume previous knowledge of graphical modeling, and so is intended to be useful to practitioners in a wide variety of fields.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Markov Random Field Modeling in Image Analysis by : Stan Z. Li
Download or read book Markov Random Field Modeling in Image Analysis written by Stan Z. Li and published by Springer Science & Business Media. This book was released on 2009-04-03 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Book Synopsis Commencement by : Iowa State University
Download or read book Commencement written by Iowa State University and published by . This book was released on 2004 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis 50 years of Statistical Physics in Mexico: Development, State of the Art and Perspectives by : Ramon Castañeda-Priego
Download or read book 50 years of Statistical Physics in Mexico: Development, State of the Art and Perspectives written by Ramon Castañeda-Priego and published by Frontiers Media SA. This book was released on 2021-09-13 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives by : Andrew Gelman
Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-10-22 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
Download or read book Statistica Sinica written by and published by . This book was released on 2002 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Graphical Models, Exponential Families, and Variational Inference by : Martin J. Wainwright
Download or read book Graphical Models, Exponential Families, and Variational Inference written by Martin J. Wainwright and published by Now Publishers Inc. This book was released on 2008 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.
Book Synopsis Modeling Spatio-Temporal Data by : Marco A. R. Ferreira
Download or read book Modeling Spatio-Temporal Data written by Marco A. R. Ferreira and published by CRC Press. This book was released on 2024-11-29 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics. Key topics: Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models. Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection. Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects. Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations. Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression. Multiscale spatio-temporal assimilation of computer model output and monitoring station data. Dynamic multiscale heteroscedastic multivariate spatio-temporal models. The M-open multiple optima paradox and some of its practical implications for multiscale modeling. Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes. The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Download or read book Machine Learning written by Maria Johnsen and published by Maria Johnsen. This book was released on 2024-07-06 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has revolutionized industries, from healthcare to entertainment, by enhancing how we understand and interact with data. Despite its prevalence, mastering this field requires both theoretical knowledge and practical skills. This book bridges that gap, starting with foundational concepts and essential mathematics, then advancing through a wide range of algorithms and techniques. It covers supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning, with clear explanations and practical examples. Real-world applications are highlighted through scenarios and case studies, demonstrating how to solve specific problems with machine learning. You'll find hands-on guides to popular tools and libraries like Python, Scikit-Learn, TensorFlow, Keras, and PyTorch, enabling you to build, evaluate, and deploy models effectively. The book explores cutting-edge topics like quantum machine learning and explainable AI, keeping you updated on the latest trends. Detailed case studies and capstone projects provide practical experience, guiding you through the entire machine learning process. This book, a labor of love born from extensive research and passion, aims to make machine learning accessible and engaging. Machine learning is about curiosity, creativity, and the pursuit of knowledge. Explore, experiment, and enjoy the journey. Thank you for choosing this book. I am excited to be part of your machine learning adventure and look forward to the incredible things you will achieve.
Book Synopsis A Survey of Statistical Network Models by : Anna Goldenberg
Download or read book A Survey of Statistical Network Models written by Anna Goldenberg and published by Now Publishers Inc. This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.
Book Synopsis Encyclopedia of Statistical Sciences, Volume 3 by :
Download or read book Encyclopedia of Statistical Sciences, Volume 3 written by and published by John Wiley & Sons. This book was released on 2005-12-16 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: ENCYCLOPEDIA OF STATISTICAL SCIENCES
Book Synopsis Markov Random Field Modeling in Computer Vision by : S.Z. Li
Download or read book Markov Random Field Modeling in Computer Vision written by S.Z. Li and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov random field (MRF) modeling provides a basis for the characterization of contextual constraints on visual interpretation and enables us to develop optimal vision algorithms systematically based on sound principles. This book presents a comprehensive study on using MRFs to solve computer vision problems, covering the following parts essential to the subject: introduction to fundamental theories, formulations of various vision models in the MRF framework, MRF parameter estimation, and optimization algorithms. Various MRF vision models are presented in a unified form, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in the subject.
Book Synopsis Statistics for Individual Crystallographic Orientation Measurements by : Karl Gerald van den Boogaart
Download or read book Statistics for Individual Crystallographic Orientation Measurements written by Karl Gerald van den Boogaart and published by . This book was released on 2002 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theory of Spatial Statistics by : M.N.M. van Lieshout
Download or read book Theory of Spatial Statistics written by M.N.M. van Lieshout and published by CRC Press. This book was released on 2019-03-19 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix. Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers. Features * Presents the mathematical foundations of spatial statistics. * Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology. * Gives pointers to the literature to facilitate further study. * Provides example code in R to encourage the student to experiment. * Offers exercises and their solutions to test and deepen understanding. The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics.
Book Synopsis Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by : Christine Sinoquet
Download or read book Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics written by Christine Sinoquet and published by OUP Oxford. This book was released on 2014-09-18 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
