Bayesian hierarchical models, Congdon, Peter D.

Автор: Lawson Andrew B.
Название: Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition
ISBN: 0367781220 ISBN-13(EAN): 9780367781224
Издательство: Taylor&Francis
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Цена: 7654.00 р.
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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: Miyamoto Sadaaki
Название: Theory of Agglomerative Hierarchical Clustering
ISBN: 9811904197 ISBN-13(EAN): 9789811904196
Издательство: Springer
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Цена: 15372.00 р.
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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: Congdon P.D.
Название: Bayesian Hierarchical Metho
ISBN: 1498785751 ISBN-13(EAN): 9781498785754
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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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
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Цена: 13275.00 р.
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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: 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
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Цена: 7348.00 р.
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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: Triantafyllopoulos Kostas
Название: Bayesian Inference of State Space Models: Kalman Filtering and Beyond
ISBN: 3030761231 ISBN-13(EAN): 9783030761233
Издательство: Springer
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Цена: 11179.00 р.
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Описание: Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models.

Автор: Mike West; Jeff Harrison
Название: Bayesian Forecasting and Dynamic Models
ISBN: 1475770987 ISBN-13(EAN): 9781475770988
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

Автор: Tatarinova Tatiana, Schumitzky Alan
Название: Nonlinear Mixture Models: A Bayesian Approach
ISBN: 1848167563 ISBN-13(EAN): 9781848167568
Издательство: World Scientific Publishing
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Цена: 14256.00 р.
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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.

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
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Цена: 15372.00 р.
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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.