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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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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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Computational Bayesian Statistics

a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion;

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Essential Bayesian Models

of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric;

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Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands;

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Bayesian Thinking, Modeling and Computation

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application;

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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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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

problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian;

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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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Statistical Computation for Environmental Sciences in R

computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical;

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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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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 Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Bayesian Missing Data Problems

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Current Trends in Bayesian Methodology with Applications

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the;

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Current Trends in Bayesian Methodology with Applications

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the;

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Bayesian Statistics An Introduction 4th

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs;

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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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Handbook of Approximate Bayesian Computation

first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and;

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An Introduction to Bayesian Inference, Methods and Computation

and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods;

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Models for Ecological Data

computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical;

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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 Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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Bayesian Methods in Health Economics

statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness;

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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 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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