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Bayesian Model Comparison

issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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Statistical Inference

solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical;

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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 Analysis in Econometrics and Statistics

and applied results on estimation, model comparison, prediction, forecasting, prior densities, model formulation and hypothesis testing;

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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 Model Selection and Statistical Modeling

different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;

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Understanding the Tripartite Approach to Bayesian Divergence Time Estimation

describe the tripartite model used for Bayesian estimation of time calibrated phylogenetic trees. The model can be readily separated into its;

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The BUGS Book

various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues;

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The BUGS Book

various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues;

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Structural Equation Modeling

, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Bayesian Networks

granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares;

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Bayesian Statistical Methods

. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized;

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Bayesian Methods for Management and Business

integrated with coverage of linear models, sensitivity analysis, Markov Chain Monte Carlo (MCMC), and model comparison. In addition, more advanced;

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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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Understanding Computational Bayesian Statistics

, and the proportional hazards model. The book begins with an outline of the similarities and differences between Bayesian and the likelihood;

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Statistics for Astrophysics

in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian;

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Bayesian Compendium

This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn't difficult;

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Bayesian Compendium

This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn't difficult;

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Bayesian Modeling Using WinBUGS

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible;

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Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems

Managing Learner Models in Adaptive Hypermedia Systems: Emerging Research and Opportunities is a collection of research on the use of Bayesian;

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The Bayesian Choice

This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition;

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Bayesian Implementation

presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation;

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Bayesian Implementation

presents a basic model of the Bayesian implementation problem and summarizes and explains recent developments in this branch of implementation;

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Practice of Bayesian Probability Theory in Geotechnical Engineering

typical problems are analyzed by using Bayesian probabilistic approach, i.e., to model the effect of initial void ratio on the soil-water;

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