Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R And Inla
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Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R And Inla
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Author : Elias Krainski
language : en
Publisher: CRC Press
Release Date : 2018-12-07
Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R And Inla written by Elias Krainski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-07 with Mathematics categories.
Describes modeling with SPDE and INLA Describes spatial and spatio-temporal models Describes multivariate models Includes detailed examples and associated R code Includes a summary on the underlying SPDE methodology R code and dataset are available from http://www.r-inla.org/spde-book
Statistical Modeling And Applications
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Author : Carlos A. Coelho
language : en
Publisher: Springer Nature
Release Date : 2024-12-17
Statistical Modeling And Applications written by Carlos A. Coelho and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-17 with Mathematics categories.
In an era defined by the seamless integration of data and sophisticated analytical and modeling techniques, the quest for advanced statistical modeling and methodologies has never been more pertinent. Statistical Modeling and Applications: Multivariate, Heavy-Tailed, Skewed Distributions, Mixture and Neural-Network Modeling, Volume 2, represents a concerted effort to bridge the gap between theoretical advancements and practical applications in the realm of Statistical Science, namely in the area of Statistical Modeling. It also aims to present a wide range of emerging topics in mathematical and statistical modeling written by a group of distinguished researchers from top-tier universities and research institutes to offer broader opportunities in stimulating further collaborations in the areas of mathematics and statistics. The book has eleven chapters, divided in two Parts, with Part I comprising five chapters dealing with the application of Multivariate Analysis techniques and multivariate distributions to a set of different situations, and Part II consisting of six chapters which address the modeling of several interesting phenomena through the use of Heavy-Tailed, Skewed, Circular-Linear and Mixture Distributions, as well as Neural Networks.
Dynamic Time Series Models Using R Inla
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Author : Nalini Ravishanker
language : en
Publisher: CRC Press
Release Date : 2022-08-10
Dynamic Time Series Models Using R Inla written by Nalini Ravishanker and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-10 with Mathematics categories.
Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.
Bayesian Inference With Inla
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Author : Virgilio Gomez-Rubio
language : en
Publisher: CRC Press
Release Date : 2020-02-20
Bayesian Inference With Inla written by Virgilio Gomez-Rubio and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-20 with Mathematics categories.
The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.
Computational Science And Its Applications Iccsa 2024 Workshops
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Author : Osvaldo Gervasi
language : en
Publisher: Springer Nature
Release Date : 2024-07-29
Computational Science And Its Applications Iccsa 2024 Workshops written by Osvaldo Gervasi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-29 with Computers categories.
This eleven-volume set LNCS 14815 – 14825 constitutes the refereed workshop proceedings of the 24th International Conference on Computational Science and Its Applications, ICCSA 2024, held at Hanoi, Vietnam, during July 1–4, 2024. The 281 full papers, 17 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 450 submissions. In addition, the conference consisted of 55 workshops, focusing on very topical issues of importance to science, technology and society: from new mathematical approaches for solving complex computational systems, to information and knowledge in the Internet of Things, new statistical and optimization methods, several Artificial Intelligence approaches, sustainability issues, smart cities and related technologies.
Spatio Temporal Statistics With R
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Author : Christopher K. Wikle
language : en
Publisher: CRC Press
Release Date : 2019-02-18
Spatio Temporal Statistics With R written by Christopher K. Wikle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-18 with Mathematics categories.
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
Spatial Statistics For Data Science
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Author : Paula Moraga
language : en
Publisher: CRC Press
Release Date : 2023-12-08
Spatial Statistics For Data Science written by Paula Moraga and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-08 with Mathematics categories.
Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners. Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches
Recent Developments In Statistics And Data Science
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Author : Regina Bispo
language : en
Publisher: Springer Nature
Release Date : 2022-11-28
Recent Developments In Statistics And Data Science written by Regina Bispo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-28 with Mathematics categories.
This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications.
Statistical Modeling Using Bayesian Latent Gaussian Models
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Author : Birgir Hrafnkelsson
language : en
Publisher: Springer Nature
Release Date : 2023-11-08
Statistical Modeling Using Bayesian Latent Gaussian Models written by Birgir Hrafnkelsson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-08 with Mathematics categories.
This book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these models is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent Gaussian models to real examples in glaciology, hydrology, engineering seismology, seismology, meteorology and climatology. These examples include: spatial predictions of surface mass balance; the estimation of Antarctica’s contribution to sea-level rise; the estimation of rating curves for the projection of water level to discharge; ground motion models for strong motion; spatial modeling of earthquake magnitudes; weather forecasting based on numerical model forecasts; and extreme value analysis of precipitation on a high-dimensional grid. The book is aimed at graduate students and experts in statistics, geophysics, environmental sciences, engineering, and related fields.
New Frontiers In Statistics And Data Science
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Author : Lígia Henriques-Rodrigues
language : en
Publisher: Springer Nature
Release Date : 2025-01-10
New Frontiers In Statistics And Data Science written by Lígia Henriques-Rodrigues and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-10 with Mathematics categories.
This volume showcases a collection of thirty-two peer-reviewed articles presented at the XXVI Congress of the Portuguese Statistical Society (2023). It covers a wide range of cutting-edge topics in both theoretical and applied statistics. Each contribution highlights the latest advancements and research in the field, offering valuable insights and innovative methodologies for researchers and practitioners alike. Readers with a background in mathematics and statistics will find it particularly beneficial, while researchers from various scientific disciplines can explore numerous robust applications.