8 edition of Hierarchical modelling for the environmental sciences found in the catalog.
Includes bibliographical references (p. 185-195) and index
|Statement||edited by James S. Clark and Alan E. Gelfand|
|Contributions||Clark, James Samuel, 1957-, Gelfand, Alan E., 1945-|
|LC Classifications||QA279.5 .H54 2006|
|The Physical Object|
|Pagination||ix, 205 p. :|
|Number of Pages||205|
|ISBN 10||0198569661, 019856967X|
|ISBN 10||9780198569664, 9780198569671|
|LC Control Number||2005030159|
Hierarchical Linear Modeling provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original "how-to" application articles following a standardized instructional format. The Guide portion consists of five chapters that provide an overview of HLM, discussion of methodological assumptions, and parallel worked. John Dunlosky, Robert Ariel, in Psychology of Learning and Motivation, Hierarchical Model of Self-Paced Study. The hierarchical model of self-paced study (Thiede & Dunlosky, ) is a precursor to the ABR emphasize that study-time allocation is goal-oriented and that learners develop agendas in an attempt to obtain their learning goals efficiently.
Sven Erik Jørgensen was the professor emeritus in environmental chemistry at the University of Copenhagen. He received a master of science in chemical engineering from the Danish Technical University (), a doctor of environmental engineering (Karlsruhe University) and a doctor of science in ecological modelling (Copenhagen University).Price: $ Book Description. Model a Wide Range of Count Time Series. Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other.
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric by: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis.
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An early contributor to the development of computational machinery for fitting hierarchical Bayesian models, his current research focuses on the analysis of spatial and spatio-temporal data. His primary areas of application are to problems in environmental science, ecology, and climatology.5/5(1).
Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications () James S. Clark, Alan Gelfand New Statistical tools are changing the wau in which scientists analyze and interpret data and models.
Many of these are emerging as a result of the wide availability of inexpensive, high speed computational power. * Provides a non-technical overview of hierarchical Bayes and Markov Chain Monte Carlo methods for analysis of environmental data * Includes chapters demonstrating the application of methods to a range of environmental challenges The book is targeted primarily at graduate level students in the environmental sciences, particularly ecology.
Hierarchical Modelling for the Environmental Sciences by James S. Clark,available at Book Depository with free delivery worldwide.2/5(1).
Hierarchical Bayes modeling with sampling based methods for ﬁtting and analysis provide a consis-tent framework for inference and prediction where information is heterogeneous and uncertain, pro-cesses are complicated, and responses depend on scale.
Nowhere are these methods more promising than in the environmental sciences. These methods. Hierarchical Modelling for the Environmental Sciences Statistical Methods and Applications Edited by James S.
Clark and Alan Gelfand. Jointly edited by a leading ecologist and statistician, with contributions from recognized experts in the field; Introduces environmental scientists to modern statistical computation techniques. These applications are based on data in ecology (), epidemiology and public health (), environmental sciences (), and economics To conclude, the second edition of Hierarchical Modeling and Analysis for Spatial Data provides an excellent treatment of methods and applications in spatial statistics.
It takes into consideration 10 years of changes (with respect to the first edition), Cited by: "The second edition of Hierarchical Modeling and Analysis for Spatial Data is a nice, rich, and excellent book, which deserves to be read by students and researchers, especially those working in the area of geosciences, environmental sciences, public health, ecology, and epidemiology.
Applied Statistics for Environmental Science with R. Product Type: Book. Edition: 1. First Published: Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS Ecological Modelling and Engineering of Lakes and Wetlands.
Product Type: Book. Edition: 1. An early contributor to the development of computational machinery for fitting hierarchical Bayesian models, his current research focuses on the analysis of spatial and spatio-temporal data. His primary areas of application are to problems in environmental science, ecology, and climatology.
2 Interactions between the Hierarchical Levels. 3 Models with Two or More Hierarchical Levels. 4 The Frequency of Disturbances Follow the Hierarchical Organization. 5 An Overview of the Models Presented in this Book.
1 Quantum Chemical Modeling in the Molecular Ecology. Introduction. Pheromone Molecules and Their Interaction With the. The Paperback of the Hierarchical Modelling for the Environmental Sciences: Statistical Methods and Applications by James S.
Clark at Barnes & Noble. Due to COVID, orders may be delayed. Thank you for your : James S. Clark. Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs.
These types of data are extremely. A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference.
Hierarchical Models in Environmental Science Article in International Statistical Review 71(2) August with 28 Reads How we measure 'reads'. Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness.
offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs.
These types of data are extremely widespread in ecology and its applications in such areas. Hierarchical modelling for the environmental sciences: statistical methods and applications. [James Samuel Clark; Alan E Gelfand;] -- New Statistical tools are changing the wau in which scientists analyze and interpret data and models.
Environmental Modeling & Assessment builds bridges between the scientific community's understanding of key environmental issues and the decision makers' need to influence relevant policies and regulations on the basis of the best available information.
This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views.
The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Get this from a library. Hierarchical modelling for the environmental sciences: statistical methods and applications.
[James Samuel Clark; Alan E Gelfand;] -- ' if you are already quite well acquainted with Bayesian concepts and terminology then this book should provide an excellent guide to the application of these advanced statistical techniques.
Al-Zaytoonah University of Jordan Amman Jordan Telephone: Fax: Email: [email protected] Student Inquiries | استفسارات الطلاب: [email protected]: [email protected] Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data.
Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step.The process and parameter stages can allow spatial and spatio‐temporal processes as well as the direct inclusion of scientific knowledge.
The paper concludes with a discussion of some outstanding problems in hierarchical modelling of environmental systems, including the need for new collaboration approaches.