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The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects;
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issues include parallel computation, Hamiltonian Monte Carlo, dynamic model selection, small sample comparison of structural models, Bayesian;
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history of econometrics, developments in time-series and cross-section econometrics, modelling with integrated variables, Bayesian econometrics;
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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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different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;
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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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problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition;
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Model (or variable) selection is an essential part of any statistical analysis. Since a GEE model does not specify a likelihood structure;
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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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of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple;
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of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple;
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A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible;
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This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook;
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well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory;
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Statistical Issues in Machine Learning Towards Reliable Split Selection and Variable Importance Measures is een boek van Strobl, Carolin;
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-art Bayesian statistics to the phylogenetics community. The book emphasizes model selection, reflecting recent interest in accurately;
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Analyze Repeated Measures Studies Using Bayesian Techniques Going beyond standard non-Bayesian books, Bayesian Methods for Repeated;
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Analyze Repeated Measures Studies Using Bayesian Techniques Going beyond standard non-Bayesian books, Bayesian Methods for Repeated;
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In-depth coverage of variable income annuities With trillions of dollars in retirement savings asset;...
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of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related;
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of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation;
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each phase of design, covering equipment selection, power distribution system analysis, conduit and conductor sizing, lighting system design;
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Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more;
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Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more;
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need for selection, applications, operations, diagnostic testing, troubleshooting and maintenance for all capital equipment placed firmly;
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Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology;
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Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology;
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