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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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Bayesian Population Analysis using WinBUGS

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program;

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Engineering Biostatistics

. Engineering Biostatistics: An Introduction using MATLAB and WinBUGS also includes: * parallel coverage of classical and Bayesian approaches, where;

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Computational Bayesian Statistics

of Bayesian software - RR-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.;

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Computational Bayesian Statistics

of Bayesian software - RR-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.;

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Bayesian Analysis with Stata

book emphasizes practical data analysis from the Bayesian perspective, and hence covers the selection of realistic priors, computational;

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Bayesian Inference for Probabilistic Risk Assessment

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing;

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Bayesian Biostatistics

various dedicated R programs. * Highlights the differences between the Bayesian and classical approaches. * Supported by an accompanying website;

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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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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

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems About This Book;

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Bayesian Psychometric Modeling

approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying;

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Bayesian Psychometric Modeling

conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid;

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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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Modern Approaches to Clinical Trials Using SAS

Get the tools you need to use SAS® in clinical trial design! Unique and multifaceted, Modern Approaches to Clinical Trials Using SAS;

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Modern Approaches to Clinical Trials Using SAS

Get the tools you need to use SAS in clinical trial design! Unique and multifaceted, Modern Approaches to Clinical Trials Using SAS;

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Time Series

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and;

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Reasoning with Data

using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and;

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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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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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Bayesian Statistics for the Social Sciences

testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches;

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Case Studies in Bayesian Statistical Modelling and Analysis

in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: * Illustrates how to do 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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Bayesian Disease Mapping

appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation. Applying Bayesian methods to the modeling;

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Bayesian Analysis with Excel and R

to take advantage of the rethinking package in R and its gateway to Stan. As you incorporate these Bayesian approaches into your;

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Bayesian Analysis For Social Sciences

implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world - and R - an open-source statistical software;

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Bayesian Modeling Using WinBUGS

theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper;

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Bayesian Model Selection and Statistical Modeling

, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods;

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