Image Analysis, Random Fields and Markov Chain Monte Carlo Methods, Gerhard Winkler
Автор: Glasserman Название: Monte Carlo Methods in Financial Engineering ISBN: 0387004513 ISBN-13(EAN): 9780387004518 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not."
Описание: This book introduces a new Markov chain optimization method with braking the detailed balance. It develops a quantum Monte Carlo method for nonconserved particles and combines it with the excitation level analysis.
Автор: Piero Barone; Arnoldo Frigessi; Mauro Piccioni Название: Stochastic Models, Statistical Methods, and Algorithms in Image Analysis ISBN: 0387978100 ISBN-13(EAN): 9780387978109 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume comprises a collection of papers by world- renowned experts on image analysis. The papers range from survey articles to research papers, and from theoretical topics such as simulated annealing through to applied image reconstruction.
Автор: Stan Z. Li Название: Markov Random Field Modeling in Image Analysis ISBN: 1849967679 ISBN-13(EAN): 9781849967679 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This detailed book presents a comprehensive study on the use of Markov Random Fields for solving computer vision problems. Various vision models are presented, and this third edition includes the most recent advances with new and expanded sections.
Автор: Kohler Название: Data Analysis Using Stata, Third Edition ISBN: 1597181102 ISBN-13(EAN): 9781597181105 Издательство: Taylor&Francis Рейтинг: Цена: 11176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks.
The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand.
Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
Описание: This book introduces a new Markov chain optimization method with braking the detailed balance. It develops a quantum Monte Carlo method for nonconserved particles and combines it with the excitation level analysis.
Автор: Gamerman, Dani. Название: Markov Chain Monte Carlo ISBN: 1584885874 ISBN-13(EAN): 9781584885870 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.
Автор: Chen Ming-Hui, Shao Qi-Man, Ibrahim Joseph G. Название: Monte Carlo Methods in Bayesian Computation ISBN: 0387989358 ISBN-13(EAN): 9780387989358 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines advanced Bayesian computational methods. It presents methods for sampling from posterior distributions and discusses how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples. This book examines each of these issues in detail and heavily focuses on computing various posterior quantities of interest from a given MCMC sample. Several topics are addressed, including techniques for MCMC sampling, Monte Carlo methods for estimation of posterior quantities, improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss computions involving model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches.The book presents an equal mixture of theory and applications involving real data. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.Ming-Hui Chen is Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute, Qu-Man Shao is Assistant Professor of Mathematics at the University of Oregon. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute.
Автор: Luc Florack; Remco Duits; Geurt Jongbloed; Marie-C Название: Mathematical Methods for Signal and Image Analysis and Representation ISBN: 1447158903 ISBN-13(EAN): 9781447158905 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a mathematical methodology for image analysis tasks at the edge of current research, including anisotropic diffusion filtering of tensor fields. Instead of specific applications, it explores methodological structures on which they are built.
Описание: Intends to bring together research directions in theoretical signal and imaging processing developed rather independently in electrical engineering, theoretical physics, mathematics and the computer sciences. This book summarizes work carried out in the field of theoretical signal and image processing.
Автор: Stan Z. Li Название: Markov Random Field Modeling in Image Analysis ISBN: 1848002785 ISBN-13(EAN): 9781848002784 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. 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.
Автор: Gerhard Winkler Название: Image Analysis, Random Fields and Dynamic Monte Carlo Methods ISBN: 3642975240 ISBN-13(EAN): 9783642975240 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This text is concerned with a probabilistic approach to image analysis as initiated by U. It formally adopts the Bayesian paradigm and therefore is referred to as `Bayesian Image Analysis`. Whereas image analysis is replete with ad hoc techniques, Bayesian image analysis provides a general framework encompassing various problems from imaging.
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