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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 Biostatistics

methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian;

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Bayesian Phylogenetics

Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research. Encouraging;

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Practical Applications of Bayesian Reliability

of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive;

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An Introduction to Bayesian Inference in Econometrics

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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Bayesian Inference in the Social Sciences

methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features;

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Bayesian Thinking, Modeling and Computation

summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Bayesian Programming

methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for;

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Bayesian Programming

methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for;

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Spatial & Spatio Temporal Bayesian

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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Time Series

in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and;

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Bayesian Model Comparison

The volume contains articles that should appeal to readers with computational, modeling, theoretical, and applied interests. Methodological;

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Advances in Political Methodology

methodology: Bayesian methods; modelling of temporal duration, dependence, and dynamics; network-analytic methodology; text, classification, and big;

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Current Trends in Symmetric Polynomials with Their Applications

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Current Trends in Symmetric Polynomials with their Applications

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Bayesian Theory and Methods with Applications

estimate of the unknown. Beyond presenting Bayesian theory and methods of analysis, the text is illustrated with a variety of applications to real;

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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 Item Response Modeling

analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with;

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Statistical Bioinformatics with R

> The inclusion of R & SAS code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the;

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Biomass in Small-Scale Energy Applications

technologies for distributed energy generation. It describes the methodology, experimental results, and computer simulations with a focus on pilot;

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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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Advances and Trends in Optimization with Engineering Applications

techniques. In recent years the theory and methodology of optimization have seen revolutionary improvements. Moreover, the exponential growth;

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Likelihood Free Methods for Cognitive Science

This book explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the;

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Bayesian Methods in Pharmaceutical Research

Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics;

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