Steve Brooks – författare

Novel Statistical Tools for Conserving and Managing PopulationsBy gathering information on key demographic parameters, scientists can often predict how populations will develop in the future and relate these parameters to external influences, such as global warming. Because of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data, modern Bayesian methods have become important in this area of statistical inference and forecasting. Emphasising model choice and model averaging, Bayesian Analysis for Population Ecology presents up-to-date methods for analysing complex ecological data. Leaders in the statistical ecology field, the authors apply the theory to a wide range of actual case studies and illustrate the methods using WinBUGS and R. The computer programs and full details of the data sets are available on the book’s website.The first part of the book focuses on models and their corresponding likelihood functions. The authors examine classical methods of inference for estimating model parameters, including maximum-likelihood estimates of parameters using numerical optimisation algorithms. After building this foundation, the authors develop the Bayesian approach for fitting models to data. They also compare Bayesian and traditional approaches to model fitting and inference.Exploring challenging problems in population ecology, this book shows how to use the latest Bayesian methods to analyse data. It enables readers to apply the methods to their own problems with confidence.

This thoroughly revised and expanded second edition of the Handbook of Markov Chain Monte Carlo reflects the dramatic evolution of MCMC methods since the publication of the first edition. With the addition of two new editors, Radu V. Craiu and Dootika Vats, this comprehensive reference now offers deeper insights into the theoretical foundations and cutting-edge developments that are reshaping the field.Key Features:Completely restructured content with 13 updated chapters from the first edition and ten entirely new chapters reflecting the latest methodological advancesIn-depth coverage of recent breakthroughs in multi-modal sampling, intractable likelihood problems, and involutive MCMC theoryComprehensive exploration of unbiased MCMC methods, control variates, and rigorous convergence boundsPractical guidance on implementing MCMC algorithms on modern hardware and software platformsCutting-edge material on the integration of MCMC with deep learning and other machine learning approachesAuthoritative treatment of theoretical foundations alongside practical implementation strategiesSupplemented by a GitHub repository including sample chapters, code, and dataThis essential reference serves statisticians, computer scientists, physicists, data scientists, and researchers across disciplines who employ computational methods for Bayesian inference and stochastic simulation. Graduate students will find it an invaluable learning resource, while experienced practitioners will appreciate its balance of theoretical depth and practical implementation advice. Whether used as a comprehensive guide to current MCMC methodology or as a reference for specific advanced techniques, this handbook provides the definitive resource for anyone working at the intersection of Bayesian computation and modern statistical modeling.

The essential field guide to British and Irish Odonata, featuring unrivalled full-colour artwork of all 58 species.'A jewel of a book' - The IndependentAdult dragonflies are among the largest and most spectacular insects alive today, and their beautiful colours, phenomenal aerobatic skills and preference for flying in warm, sunny weather make them easy to observe. Now in its sixth edition, this trusted, practical guide enables you to identify all 58 species across the British Isles, featuring fascinating descriptive accounts and helpful insect-watching tips. It brings together more than 300 exquisitely detailed illustrations by renowned wildlife artist Richard Lewington alongside 168 stunning photographs to showcase the essential identification characteristics. Regional guides, flight-period charts and distribution maps help you to determine when and where to see each species, and an identification key to the larvae of all the resident and migratory dragonfly and damselfly species is also provided.Whether you are just beginning to explore the world of dragonflies and damselflies or are a long-time enthusiast, this an indispensable companion for discovering and identifying these remarkable insects.

The essential field guide to British and Irish Odonata, featuring unrivalled full-colour artwork of all 58 species.'A jewel of a book' - The IndependentAdult dragonflies are among the largest and most spectacular insects alive today, and their beautiful colours, phenomenal aerobatic skills and preference for flying in warm, sunny weather make them easy to observe. Now in its sixth edition, this trusted, practical guide enables you to identify all 58 species across the British Isles, featuring fascinating descriptive accounts and helpful insect-watching tips. It brings together more than 300 exquisitely detailed illustrations by renowned wildlife artist Richard Lewington alongside 168 stunning photographs to showcase the essential identification characteristics. Regional guides, flight-period charts and distribution maps help you to determine when and where to see each species, and an identification key to the larvae of all the resident and migratory dragonfly and damselfly species is also provided.Whether you are just beginning to explore the world of dragonflies and damselflies or are a long-time enthusiast, this an indispensable companion for discovering and identifying these remarkable insects.

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory.The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology.The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.