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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 Approaches in Oncology Using R and OpenBUGS

bayesian approach implementation. For those who have never used R/OpenBUGS, the book begins with a self-contained introduction to R that lays the;

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Exposure-Response Modeling

Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships Written by an applied statistician with;

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Exposure-Response Modeling

Discover the Latest Statistical Approaches for Modeling Exposure-Response Relationships Written by an applied statistician with;

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Quantitative Evaluation of Safety in Drug Development

Evaluation of Safety in Drug Development: Design, Analysis and Reporting explains design, monitoring, analysis, and reporting issues for both;

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Bayesian Adaptive Methods for Clinical Trials

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a;

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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 Applications in Pharmaceutical Development

ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.;

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Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R

for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for;

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R for Marketing Research and Analytics

modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a;

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Bayesian Analysis with Excel and R

Leverage the full power of Bayesian analysis for competitive advantage Bayesian methods can solve problems you can't reliably handle;

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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 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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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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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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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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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 Statistics for the Social Sciences

. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a;

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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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Introduction to Bayesian Estimation and Copula Models of Dependence

, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management;

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Statistical Methods in Drug Combination Studies

combination studies. The book is equally useful for statisticians to become better equipped to deal with drug combination study design and analysis;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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