Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts.The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. (-) Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation (-) Brings together experts from a broad range of fields (-) Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species (-) Helps you choose the right software (-) Draws on numerous examples based on real biological datasets

Ecology --- Evolution --- Meta-analysis. --- Statistical methods. --- Mathematical models. --- Science --- Mathematics --- Life Sciences --- Ecology. --- Evolution. --- Probability & Statistics --- General. --- Meta-Analysis --- Statistical methods --- Mathematical models --- Ecology - Statistical methods --- Evolution - Mathematical models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. .

Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Statistics . --- Probabilities. --- Bayesian Inference. --- Probability Theory and Stochastic Processes. --- Applied Statistics. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is a source of information on practical and innovative approaches to biosecurity surveillance. It explains the foundation and concepts behind surveillance design, with examples of methods and tools created to deal with surveillance challenges. With supporting case studies and including current directions in research, it covers evidence-based approaches to surveillance, statistics, detectability, single and multi-species detection, risk assessment, diagnostics, data-basing, modelling of invasion and spread, optimisation, and future climate challenges.

Biological invasions --- Plant invasions --- Environmental monitoring. --- Prevention. --- Prevention.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. .

Statistical science --- Probability theory --- waarschijnlijkheidstheorie --- statistiek