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Author: Franzi Korner-Nievergelt
Publisher: Academic Press
ISBN: 0128016787
Category : Science
Languages : en
Pages : 329
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Book Description
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco
Author: Franzi Korner-Nievergelt
Publisher: Academic Press
ISBN: 0128016787
Category : Science
Languages : en
Pages : 329
Get Book Here
Book Description
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco
Author: Joseph M. Hilbe
Publisher: Cambridge University Press
ISBN: 1108210740
Category : Mathematics
Languages : en
Pages : 429
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Book Description
This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.
Author: Richard E. Plant
Publisher: CRC Press
ISBN: 1351189905
Category : Science
Languages : en
Pages : 685
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Book Description
Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.
Author: M. Antónia Amaral Turkman
Publisher: Cambridge University Press
ISBN: 1108574610
Category : Computers
Languages : en
Pages : 256
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Book Description
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.
Author: Marc Kéry
Publisher: Academic Press
ISBN: 0128014865
Category : Science
Languages : en
Pages : 810
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Book Description
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 as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. - Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection - Presents models and methods for identifying unmarked individuals and species - Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses - Includes companion website containing data sets, code, solutions to exercises, and further information
Author: Sylvia Fruhwirth-Schnatter
Publisher: CRC Press
ISBN: 0429508867
Category : Computers
Languages : en
Pages : 489
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Book Description
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
Author:
Publisher: Academic Press
ISBN: 0323952690
Category : Mathematics
Languages : en
Pages : 322
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Book Description
Advancements in Bayesian Methods and Implementation, Volume 47 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Fisher Information, Cramer-Rao and Bayesian Paradigm, Compound beta binomial distribution functions, MCMC for GLMMS, Signal Processing and Bayesian, Mathematical theory of Bayesian statistics where all models are wrong, Machine Learning and Bayesian, Non-parametric Bayes, Bayesian testing, and Data Analysis with humans, Variational inference or Functional horseshoe, Generalized Bayes. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Statistics series - Updated release includes the latest information on Advancements in Bayesian Methods and Implementation
Author: Michael Schaub
Publisher: Academic Press
ISBN: 0128209151
Category : Science
Languages : en
Pages : 640
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Book Description
Integrated Population Models: Theory and Ecological Applications with R and JAGS is the first book on integrated population models, which constitute a powerful framework for combining multiple data sets from the population and the individual levels to estimate demographic parameters, and population size and trends. These models identify drivers of population dynamics and forecast the composition and trajectory of a population. Written by two population ecologists with expertise on integrated population modeling, this book provides a comprehensive synthesis of the relevant theory of integrated population models with an extensive overview of practical applications, using Bayesian methods by means of case studies. The book contains fully-documented, complete code for fitting all models in the free software, R and JAGS. It also includes all required code for pre- and post-model-fitting analysis. Integrated Population Models is an invaluable reference for researchers and practitioners involved in population analysis, and for graduate-level students in ecology, conservation biology, wildlife management, and related fields. The text is ideal for self-study and advanced graduate-level courses. - Offers practical and accessible ecological applications of IPMs (integrated population models) - Provides full documentation of analyzed code in the Bayesian framework - Written and structured for an easy approach to the subject, especially for non-statisticians
Author: David A. MacLean
Publisher: MDPI
ISBN: 3039280961
Category : Science
Languages : en
Pages : 220
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Book Description
Spruce budworm (Choristoneura fumiferana (Clem.)) outbreaks are a dominant natural disturbance in the forests of Canada and northeastern USA. Widespread, severe defoliation by this native insect results in large-scale mortality and growth reductions of spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.) forests, and largely determines future age–class structure and productivity. The last major spruce budworm outbreak defoliated over 58 million hectares in the 1970s–1980s, and caused 32–43 million m3/year of timber volume losses from 1978 to 1987, in Canada. Management to deal with spruce budworm outbreaks has emphasized forest protection, spraying registered insecticides to prevent defoliation and keep trees alive. Other tactics can include salvage harvesting, altering harvest schedules to remove the most susceptible stands, or reducing future susceptibility by planting or thinning. Chemical insecticides are no longer used, and protection strategies use biological insecticides Bacillus thuringiensis (B.t.) or tebufenozide, a specific insect growth regulator. Over the last five years, a $30 million research project has tested another possible management tactic, termed an ‘early intervention strategy’, aimed at area-wide management of spruce budworm populations. This includes intensive monitoring to detect ‘hot spots’ of rising budworm populations before defoliation occurs, targeted insecticide treatment to prevent spread, and detailed research into target and non-target insect effects. The objective of this Special Issue is to compile the most recent research on protection strategies against spruce budworm. A series of papers will describe results and prospects for the use of an early intervention strategy in spruce budworm and other insect management.
Author: Michael Carl
Publisher: Springer Nature
ISBN: 3030697770
Category : Language Arts & Disciplines
Languages : en
Pages : 436
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Book Description
This book assembles fifteen original, interdisciplinary research chapters that explore methodological and conceptual considerations as well as user and usage studies to elucidate the relation between the translation product and translation/post-editing processes. It introduces numerous innovative empirical/data-driven measures as well as novel classification schemes and taxonomies to investigate and quantify the relation between translation quality and translation effort in from-scratch translation, machine translation post-editing and computer-assisted audiovisual translation. The volume addresses questions in the translation of cognates, neologisms, metaphors, and idioms, as well as figurative and cultural specific expressions. It re-assesses the notion of translation universals and translation literality, elaborates on the definition of translation units and syntactic equivalence, and investigates the impact of translation ambiguity and translation entropy. The results and findings are interpreted in the context of psycho-linguistic models of bilingualism and re-frame empirical translation process research within the context of modern dynamic cognitive theories of the mind. The volume bridges the gap between translation process research and machine translation research. It appeals to students and researchers in the fields.
