Автор: Mike West; Jeff Harrison Название: Bayesian Forecasting and Dynamic Models ISBN: 1475770987 ISBN-13(EAN): 9781475770988 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.
Автор: Congdon P.D. Название: Bayesian Hierarchical Metho ISBN: 1498785751 ISBN-13(EAN): 9781498785754 Издательство: Taylor&Francis Рейтинг: Цена: 117390.00 T Наличие на складе: Нет в наличии. Описание: This is the second edition of a book on applied Bayesian modelling using WinBUGS. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies.
Автор: Erik Vanem; Elzbieta Maria Bitner-Gregersen; Chris Название: Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height ISBN: 3662521970 ISBN-13(EAN): 9783662521977 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides an example of a thorough statistical treatment of ocean wave data in space and time. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what that effect might be.
Автор: Lawson Andrew B. Название: Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition ISBN: 0367781220 ISBN-13(EAN): 9780367781224 Издательство: Taylor&Francis Рейтинг: Цена: 51030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, the third edition provides an up-to-date, cohesive account of the full range of Bayesian disease m
Автор: Lee, Youngjo Ronnegard, Lars Noh, Maengseok Название: Data analysis using hierarchical generalized linear models with r ISBN: 0367657929 ISBN-13(EAN): 9780367657925 Издательство: Taylor&Francis Рейтинг: Цена: 48990.00 T Наличие на складе: Нет в наличии. Описание: Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to
Автор: Miyamoto Sadaaki Название: Theory of Agglomerative Hierarchical Clustering ISBN: 9811904197 ISBN-13(EAN): 9789811904196 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses recent theoretical developments in agglomerative hierarchical clustering. A fundamental theorem for single linkage using a fuzzy graph is proved, which uncovers several theoretical features of single linkage.
Автор: Triantafyllopoulos Kostas Название: Bayesian Inference of State Space Models: Kalman Filtering and Beyond ISBN: 3030761231 ISBN-13(EAN): 9783030761233 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models.
Bayesian Data Analysis in Ecology Using Linear Modelswith R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Modelswith R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions--including all R codes--that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.
Автор: L. Bauwens Название: Bayesian Full Information Analysis of Simultaneous Equation Models Using Integration by Monte Carlo ISBN: 3540133844 ISBN-13(EAN): 9783540133841 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Tatarinova Tatiana, Schumitzky Alan Название: Nonlinear Mixture Models: A Bayesian Approach ISBN: 1848167563 ISBN-13(EAN): 9781848167568 Издательство: World Scientific Publishing Рейтинг: Цена: 95040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides an introduction to the important subject of nonlinear mixture models from a Bayesian perspective. This title contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications.
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