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Bayesian Population Analysis Using Winbugs

Author: Marc Kéry
Publisher: Academic Press
ISBN: 0123870208
Size: 62.86 MB
Format: PDF, Kindle
View: 419
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Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS, and its open-source sister OpenBugs, is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist All WinBUGS/OpenBUGS analyses are completely integrated in software R Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R

Introduction To Hierarchical Bayesian Modeling For Ecological Data

Author: Eric Parent
Publisher: CRC Press
ISBN: 1584889195
Size: 47.14 MB
Format: PDF, Mobi
View: 5869
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.

Learning Analytics

Author: Johann Ari Larusson
Publisher: Springer
ISBN: 1461433053
Size: 16.36 MB
Format: PDF, ePub, Docs
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In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.

Reintroduction Of Fish And Wildlife Populations

Author: David S. Jachowski
Publisher: Univ of California Press
ISBN: 0520284615
Size: 17.47 MB
Format: PDF
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Reintroduction of Fish and Wildlife Populations provides a practical step-by-step guide to successfully planning, implementing, and evaluating the reestablishment of animal populations in former habitats or their introduction in new environments. In each chapter, experts in reintroduction biology outline a comprehensive synthesis of core concepts, issues, techniques, and perspectives. This manual and reference supports scientists and managers from fisheries and wildlife professions as they plan reintroductions, initiate releases of individuals, and manage restored populations over time. Covering a broad range of taxonomic groups, ecosystems, and global regions, this edited volume is an essential guide for academics, students, and professionals in natural resource management.

Bayesian Analysis Made Simple

Author: Phil Woodward
Publisher: CRC Press
ISBN: 1439839557
Size: 74.20 MB
Format: PDF, ePub
View: 2479
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Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand. Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do not have the time to create WinBUGS code and ancillary files for every analysis they undertake. Accessible to even those who would not routinely use Excel, this book provides a custom-made Excel GUI, immediately useful to those users who want to be able to quickly apply Bayesian methods without being distracted by computing or mathematical issues. From simple NLMs to complex GLMMs and beyond, Bayesian Analysis Made Simple describes how to use Excel for a vast range of Bayesian models in an intuitive manner accessible to the statistically savvy user. Packed with relevant case studies, this book is for any data analyst wishing to apply Bayesian methods to analyze their data, from professional statisticians to statistically aware scientists.

Bayesian Methods In Health Economics

Author: Gianluca Baio
Publisher: CRC Press
ISBN: 1439895554
Size: 40.29 MB
Format: PDF
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Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

Bayesian Analysis For Population Ecology

Author: Ruth King
Publisher: Chapman & Hall
ISBN:
Size: 75.92 MB
Format: PDF
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Modern Bayesian methods have an important role to play in population ecology. Statistical methods for the analysis of mark-recapture-recovery data on wild animals continue to develop in response to the availability of long-term data sets and advances in animal marking and tracking techniques. Bringing together top experts in the field, this book presents up-to-date Bayesian procedures in an accessible manner, illustrated by a wide range of real examples and complemented by accessible computer programs. The authors include WinBUGS and R code for all of the analyses that are performed in the text.