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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: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;
Vergelijkbare producten zoals Bayesian Missing Data Problems
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;
Vergelijkbare producten zoals Missing Data Analysis in Practice
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;
Vergelijkbare producten zoals Missing Data Analysis in Practice
sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.;
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error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;
Vergelijkbare producten zoals Bayesian Modeling in Bioinformatics
error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;
Vergelijkbare producten zoals Bayesian Modeling in Bioinformatics
of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data;
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of their ability to easily incorporate random effects, fit state-space models, evaluate posterior model probabilities, and deal with missing data;
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, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;
Vergelijkbare producten zoals Missing Data Methods
, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian;
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An introduction to missing data in statistical applications is given in the beginning. The main part of the book deals with selection;
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in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data;
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The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;
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This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation;
Vergelijkbare producten zoals Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives
Students, researchers and data scientists who wish to learn Bayesian data analysis with Python and implement probabilistic models in their day to;
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Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current;
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traditional techniques for missing data inference including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods and applies the;
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limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers;
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, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data;
Vergelijkbare producten zoals Analysis of Incomplete Multivariate Data
, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data;
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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;
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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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cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular;
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of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop;
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presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ;
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problems, from new product introduction to pricing, and with a wide variety of different data sources. Bayesian Statistics and Marketing describes;
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