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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 serves two audiences: those who are familiar with the theory and applications;
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distribution of decision variables in a problem * Model a problem using Bayesian Linear Regression approach with the R package BLR * Use Bayesian;
Vergelijkbare producten zoals 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
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 examines the Bayesian and frequentist methods;
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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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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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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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This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts;
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Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;
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There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase;
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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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Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and;
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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: 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: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems;
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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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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: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian;
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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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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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identifying processing deficits Understanding how the brain processes information is vital to identifying a child's strengths and weaknesses with;
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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 provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;
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Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis;
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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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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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