Image Analysis, Random Fields and Dynamic Monte Carlo Methods, Gerhard Winkler
Автор: Durbin, James; Koopman, Siem Jan Название: Time Series Analysis by State Space Methods ISBN: 019964117X ISBN-13(EAN): 9780199641178 Издательство: Oxford Academ Рейтинг: Цена: 18216.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
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."
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.
Описание: This book illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Conveys a sound understanding of of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling.
Описание: In their review of the "Bayesian analysis of simultaneous equation systems", Dr ze and Richard (1983) - hereafter DR - express the following viewpoint about the present state of development of the Bayesian full information analysis of such sys- tems i) the method allows "a flexible specification of the prior density, including well defined noninformative prior measures"; ii) it yields "exact finite sample posterior and predictive densities". However, they call for further developments so that these densities can be eval- uated through 'numerical methods, using an integrated software packa e. To that end, they recommend the use of a Monte Carlo technique, since van Dijk and Kloek (1980) have demonstrated that "the integrations can be done and how they are done". In this monograph, we explain how we contribute to achieve the developments suggested by Dr ze and Richard. A basic idea is to use known properties of the porterior density of the param- eters of the structural form to design the importance functions, i. e. approximations of the posterior density, that are needed for organizing the integrations.
Автор: Carlo Braccini; Leila DeFloriani; Gianni Vernazza Название: Image Analysis and Processing ISBN: 3540602984 ISBN-13(EAN): 9783540602989 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: These proceedings provide a state-of-the-art report on all current issues of image analysis and processing. Theoretical aspects are addressed, as well as systems design and advanced applications, particularly in medical imaging.
Автор: Rubinstein Reuven Y Название: Fast Sequential Monte Carlo Methods for Counting and Optimiz ISBN: 1118612264 ISBN-13(EAN): 9781118612262 Издательство: Wiley Рейтинг: Цена: 16307.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the first comprehensive account of fast sequential Monte Carlo (SMC) methods for counting and optimization at an exceptionally accessible level. Written by authorities in the field, it places great emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration.
Автор: Kathy McQuillen Martensen Название: Workbook for Radiographic Image Analysis ISBN: 0323280714 ISBN-13(EAN): 9780323280716 Издательство: Elsevier Science Рейтинг: Цена: 9283.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The companion workbook for Radiographic Analysis, 3rd Edition, provides you with ample opportunities to practice and apply information from the text. With study questions, additional suboptimal images for analysis, and an answer key to guide you through the problems, you'll have all the tools you need to hone your imaging and evaluation skills. Positioning and technique exercises prepare you for success in radiography practice.
Suboptimal images with questions ensure you know and understand what features need to be visible in an image and how to adjust when the images are incorrect or poor. Extra images offer additional practice with identifying poor quality images and recognizing how they are produced. Study questions reinforce text material and prepare you for certification.
NEW! More suboptimal images for analysis and correction help you hone your evaluation skills. NEW! Expansion of pediatric, obesity, and trauma sections provide pertinent information needed for clinical success.
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.
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: Simon M. Lin (Editor), Kimberly F. Johnson Название: Methods of Microarray Data Analysis II: Papers from CAMDA `01 ISBN: 1475788312 ISBN-13(EAN): 9781475788310 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter.
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