A Bayesian Framework For Multimodel Regression

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A Bayesian Framework For Multimodel Regression

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Author : Timothy DelSole
language : en
Publisher:
Release Date : 2006

A Bayesian Framework For Multimodel Regression written by Timothy DelSole and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Bayesian statistical decision theory categories.

Large Scale Machine Learning In The Earth Sciences

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Author : Ashok N. Srivastava
language : en
Publisher: CRC Press
Release Date : 2017-08-01

Large Scale Machine Learning In The Earth Sciences written by Ashok N. Srivastava and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-01 with Computers categories.

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Model Selection And Multimodel Inference

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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-12-04

Model Selection And Multimodel Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-04 with Mathematics categories.

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan

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Author : Franzi Korner-Nievergelt
language : en
Publisher: Academic Press
Release Date : 2015-04-04

Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan written by Franzi Korner-Nievergelt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-04 with Science categories.

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

Proceedings Of The Ecmwf Workshop On Representing Model Uncertainty And Error In Numerical Weather And Climate Prediction Models

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Author :
language : en
Publisher:
Release Date : 2011

Proceedings Of The Ecmwf Workshop On Representing Model Uncertainty And Error In Numerical Weather And Climate Prediction Models written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Climatology categories.

The Impact Of Air Sea Interactions On The Simulation Of Tropical Intraseasonal Variability

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Author : Kathy Pegion
language : en
Publisher:
Release Date : 2007

The Impact Of Air Sea Interactions On The Simulation Of Tropical Intraseasonal Variability written by Kathy Pegion and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Climatic changes categories.

Advances In Observations And Modeling Of Physical Processes In The Marine Environment

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Author : Guihua Wang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-08

Advances In Observations And Modeling Of Physical Processes In The Marine Environment written by Guihua Wang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-08 with Science categories.

Statistical Postprocessing Of Ensemble Forecasts

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Author : Stéphane Vannitsem
language : en
Publisher: Elsevier
Release Date : 2018-05-17

Statistical Postprocessing Of Ensemble Forecasts written by Stéphane Vannitsem and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-17 with Science categories.

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner

The Modulated Annual Cycle An Alternative Reference Frame For Climate Anomalies

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Author : Zhaohua Wu
language : en
Publisher:
Release Date : 2007

The Modulated Annual Cycle An Alternative Reference Frame For Climate Anomalies written by Zhaohua Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Climatic changes categories.

Bayesian Models

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Author : N. Thompson Hobbs
language : en
Publisher: Princeton University Press
Release Date : 2025-06-03

Bayesian Models written by N. Thompson Hobbs and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Science categories.

A fully updated and expanded edition of the essential primer on Bayesian modeling for ecologists Uniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This book teaches the basic principles of mathematics and statistics needed to apply Bayesian models to the analysis of ecological data, using language non-statisticians can understand. Deemphasizing computer coding in favor of a clear treatment of model building, it starts with a definition of probability and proceeds step-by-step through distribution theory, likelihood, simple Bayesian models, and hierarchical Bayesian models. Now revised and expanded, Bayesian Models enables students and practitioners to gain new insights from ecological models and data properly tempered by uncertainty. Covers the basic rules of probability needed to model diverse types of ecological data in the Bayesian framework Shows how to write proper mathematical expressions for posterior distributions using directed acyclic graphs as templates Explains how to use the powerful Markov chain Monte Carlo algorithm to find posterior distributions of model parameters, latent states, and missing data Teaches how to check models to assure they meet the assumptions of model-based inference Demonstrates how to make inferences from single and multiple Bayesian models Provides worked problems for practicing and strengthening modeling skills Features new chapters on spatial models and modeling missing data