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Bayesian Missing Data Problems

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

Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Missing Data in Longitudinal Studies

sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Bayesian Analysis for Population Ecology

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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Bayesian Analysis for Population Ecology

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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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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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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Selection Models for Nonignorable Missing Data

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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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data;

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Bayesian Models for Categorical Data

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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Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation;

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Bayesian Analysis with Python

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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Statistical Analysis with Missing Data

Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current;

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Applied Missing Data Analysis in the Health Sciences

traditional techniques for missing data inference including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods and applies the;

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Contemporary Bayesian Econometrics And Statistics

limited or imperfect data. The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers;

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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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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

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods;

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Bayesian Methods for Management and Business

HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATA Featuring an accessible approach, Bayesian Methods for;

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Handbook of Missing Data Methodology

cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular;

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Bayesian Artificial Intelligence

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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Bayesian Artificial Intelligence

presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ;

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Bayesian Statistics and Marketing

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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