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Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives

scores, instrumental variables and Bayesian inference.* Includes a number of applications from the social and health sciences.* Edited and;

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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 Programming

principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from;

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Bayesian Programming

principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from;

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Missing Data in Longitudinal Studies

Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal;

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Bayesian Data Analysis

is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research;

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Inference and Intervention

Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications;

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Inference and Intervention

Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications;

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Bayesian Modeling in Bioinformatics

Bayesian kernel machines, and QTL mapping. Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume;

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Bayesian Modeling in Bioinformatics

Bayesian kernel machines, and QTL mapping. Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume;

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Discovery And Fusion Of Uncertain Knowledge In Data

systems to support the real world applications. However, the process involves knowledge acquisition, representation, inference and data, Bayesian;

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Bayesian Inference in the Social Sciences

analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation;

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Likelihood and Bayesian Inference

choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book Applied Statistical Inference has been;

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Likelihood & Bayesian Inference

choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book Applied Statistical Inference has been;

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Statistical Rethinking

interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian;

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Probability and Bayesian Modeling

distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources;

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Introduction to Hierarchical Bayesian Modeling for Ecological Data

Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources;

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Missing Data Analysis in Practice

error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.;

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Missing Data Analysis in Practice

error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.;

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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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Bayesian Disease Mapping

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling;

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Exposure-Response Modeling

also covers Bayesian analysis and model-based Bayesian decision analysis, causal inference to eliminate confounding biases, and exposure;

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Exposure-Response Modeling

also covers Bayesian analysis and model-based Bayesian decision analysis, causal inference to eliminate confounding biases, and exposure;

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Bayesian Methods For Hackers

algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to;

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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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