Bayesian hierarchical models : with applications using R,

Bayesian Hierarchical Models: With Appli

📁 Bayesian Hierarchical Models: With Applications Using R

✍ Peter D. Congdon 📂 Library 📅 2020 🏛 Chapman & Hall/CRC Press/Taylor & Francis Group 🌐 English

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections.

Bayesian Hierarchical Models-With Applic

📁 Bayesian Hierarchical Models-With Applications Using R, Second Edition

✍ Peter D. Congdon (Author) 📂 Library 📅 2019 🏛 Chapman and Hall/CRC

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collection

Bayesian Hierarchical Space-Time Models

📁 Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height

✍ Erik Vanem (auth.) 📂 Library 📅 2013 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependenc

Data analysis using hierarchical general

📁 Data analysis using hierarchical generalized linear models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/;CRC Press 🌐 English

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the

Data analysis using hierarchical general

📁 Data analysis using hierarchical generalized linear models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/;CRC Press 🌐 English

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the

Data Analysis Using Hierarchical General

📁 Data Analysis Using Hierarchical Generalized Linear Models with R

✍ Youngjo Lee, Lars Ronnegard, Maengseok Noh 📂 Library 📅 2017 🏛 Chapman and Hall/CRC 🌐 English

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the

Bayesian hierarchical models : with applications using R,

✦ Synopsis