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New Frontiers in Bayesian Statistics

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Cover of 'New Frontiers in Bayesian Statistics'

Table of Contents

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    Book Overview
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    Chapter 1 Approximate Bayesian Algorithm for Tensor Robust Principal Component Analysis
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    Chapter 2 Bayesian Quantile Regression for Big Data Analysis
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    Chapter 3 Towards a Bayesian Analysis of Migration Pathways Using Chain Event Graphs of Agent Based Models
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    Chapter 4 Power-Expected-Posterior Methodology with Baseline Shrinkage Priors
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    Chapter 5 Bayesian Nonparametric Scalar-on-Image Regression via Potts-Gibbs Random Partition Models
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    Chapter 6 Block Structured Graph Priors in Gaussian Graphical Models
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    Chapter 7 A Bayesian Joint Spatio-temporal Model for Multiple Mosquito-Borne Diseases
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    Chapter 8 A Bayesian Nonparametric Test for Cross-Group Differences Relative to a Control
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    Chapter 9 Specification of the Base Measure of Nonparametric Priors via Random Means
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    Chapter 10 Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection
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    Chapter 11 Bayesian Growth Curve Model for Studying the Intra-abdominal Volume During Pneumoperitoneum for Laparoscopic Surgery
Attention for Chapter 1: Approximate Bayesian Algorithm for Tensor Robust Principal Component Analysis
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Chapter title
Approximate Bayesian Algorithm for Tensor Robust Principal Component Analysis
Chapter number 1
Book title
New Frontiers in Bayesian Statistics
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-16427-9_1
Book ISBNs
978-3-03-116426-2, 978-3-03-116427-9
Authors

Srakar, Andrej

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