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Bayesian Essentials with R

readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It;

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Bayesian Approaches in Oncology Using R and OpenBUGS

Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications;

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Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

distribution of decision variables in a problem * Model a problem using Bayesian Linear Regression approach with the R package BLR * Use Bayesian;

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Spatial & Spatio Temporal Bayesian

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the;

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;

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Bayesian Analysis with Excel and R

of Excel's Bayesian capabilities and move toward R to do even more. Step by step, with real-world examples, Carlberg shows you how to use;

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Computational Bayesian Statistics

underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the;

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Computational Bayesian Statistics

underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the;

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Statistics for Astrophysics

This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts;

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Bayesian Model Selection and Statistical Modeling

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;

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Bayesian Computation with R

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase;

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Practical Applications of Bayesian Reliability

of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical;

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Bayesian Statistics for the Social Sciences

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and;

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Doing Bayesian Data Analysis

Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first;

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Bayesian Analysis with R for Drug Development

, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems;

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Bayesian Analysis with R for Drug Development

, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems;

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Bayesian Hierarchical Models

and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Bayesian Hierarchical Models

data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Doing Bayesian Data Analysis

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian;

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Introduction to Bayesian Statistics

field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested;

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Essentials of Processing Assessment

identifying processing deficits Understanding how the brain processes information is vital to identifying a child's strengths and weaknesses with;

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Bayesian Methods

An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Bayesian Statistical Methods

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;

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Bayesian Model Selection and Statistical Modeling

Ando shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the;

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Introduction to Bayesian Estimation and Copula Models of Dependence

readers with the tools needed to implement the procedures of Bayesian estimation in copula models of dependence. This book is structured in two;

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