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Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the;
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Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the;
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statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly;
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combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the;
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methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian;
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volume is an early introduction to Bayesian inference and methodology which still has lasting value for today's statistician and student. The;
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methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for;
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methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for;
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>Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and;
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subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social;
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inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a;
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inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a;
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introduction to Bayesian methods of categorical outcomes. Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial;
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This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical;
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reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery;
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reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery;
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of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive;
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methodology: Bayesian methods; modelling of temporal duration, dependence, and dynamics; network-analytic methodology; text, classification, and big;
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methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features;
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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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structure fits well in Bayesian methodology. Thus, the book takes the Bayesian approach and develops the Markov chain Monte Carlo method to conduct;
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grow in statistical analysis. It intended to make a single source of information on Bayesian statistical methodology for oncology research to;
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Volume I of the Palgrave Handbook of Econometrics covers developments in theoretical econometrics, including essays on the methodology and;
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This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the;
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Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a;
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HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATA Featuring an accessible approach, Bayesian Methods for;
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