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The Resource Bayesian Nonparametric Data Analysis

Bayesian Nonparametric Data Analysis

Label
Bayesian Nonparametric Data Analysis
Title
Bayesian Nonparametric Data Analysis
Creator
Contributor
Subject
Language
eng
Summary
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book's structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages
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Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Springer Series in Statistics Ser
Bayesian Nonparametric Data Analysis
Label
Bayesian Nonparametric Data Analysis
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=2096787
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Preface -- Acronyms -- Contents -- 1 Introduction -- References -- 2 Density Estimation: DP Models -- 2.1 Dirichlet Process -- 2.1.1 Definition -- 2.1.2 Posterior and Marginal Distributions -- 2.2 Dirichlet Process Mixture -- 2.2.1 The DPM Model -- 2.2.2 Mixture of DPM -- 2.3 Clustering Under the DPM -- 2.4 Posterior Simulation for DPM Models -- 2.4.1 Conjugate DPM Models -- 2.4.2 Updating Hyper-Parameters -- 2.4.3 Non-Conjugate DPM Models -- 2.4.4 Neal's Algorithm 8 -- 2.4.5 Slice Sampler -- 2.4.6 Finite DP -- 2.5 Generalizations of the Dirichlet Processes -- 2.5.1 Tail-Free Processes -- 2.5.2 Species Sampling Models (SSM) -- 2.5.3 Generalized Dirichlet Processes -- References -- 3 Density Estimation: Models Beyond the DP -- 3.1 Polya Trees -- 3.1.1 Definition -- 3.1.2 Prior Centering -- 3.1.3 Posterior Updating and Marginal Model -- 3.2 Variations of the Polya Tree Processes -- 3.2.1 Mixture of Polya Trees -- 3.2.2 Partially Specified Polya Trees -- 3.3 Posterior Simulation for Polya Tree Models -- 3.3.1 FTP and i.i.d. Sampling -- 3.3.2 PT and MPT Prior Under i.i.d. Sampling -- 3.3.3 Posterior Inference for Non-Conjugate Models -- 3.4 A Comparison of DP Versus PT Models -- 3.5 NRMI -- 3.5.1 Other Generalizations of the DP Prior -- 3.5.2 Mixture of NRMI -- References -- 4 Regression -- 4.1 Bayesian Nonparametric Regression -- 4.2 Nonparametric Residual Distribution -- 4.3 Nonparametric Mean Function -- 4.3.1 Basis Expansions -- Wavelets -- Neural Networks -- Other Basis Expansions -- 4.3.2 B-Splines -- 4.3.3 Gaussian Process Priors -- 4.3.4 Regression Trees -- 4.4 Fully Nonparametric Regression -- 4.4.1 Priors on Families of Random Probability Measures -- Posterior Simulation Under a DDP model -- 4.4.2 ANOVA DDP and LDDP -- Posterior MCMC for the ANOVA DDP Model -- 4.4.3 Dependent PT Prior -- 4.4.4 Conditional Regression -- References
  • 5 Categorical Data -- 5.1 Categorical Responses Without Covariates -- 5.1.1 Binomial Responses -- 5.1.2 Categorical Responses -- 5.1.3 Multivariate Ordinal Data -- 5.2 Categorical Responses with Covariates -- 5.2.1 Nonparametric Link Function: A Semiparametric GLM -- 5.2.2 Models for Latent Scores -- 5.2.3 Nonparametric Random Effects Model -- 5.2.4 Multivariate Ordinal Regression -- 5.3 ROC Curve Estimation -- References -- 6 Survival Analysis -- 6.1 Distribution Estimation for Event Times -- 6.1.1 Neutral to the Right Processes -- 6.1.2 Dependent Increments Models -- 6.2 Semiparametric Survival Regression -- 6.2.1 Proportional Hazards -- 6.2.2 Accelerated Failure Time -- 6.2.3 Proportional Odds -- 6.2.4 Other Semiparametric Models and Extensions -- 6.3 Fully Nonparametric Survival Regression -- 6.3.1 Extensions of the AFT model -- 6.3.2 ANCOVA-DDP: Linear Dependent DP Mixture -- 6.3.3 Linear Dependent Tail-Free Process (LDTFP) -- 6.4 More Examples -- 6.4.1 Example 17: Breast Retraction Data -- 6.4.2 Example 8 (ctd.): Breast Cancer Trial -- 6.4.3 Example 18: Lung Cancer Data -- References -- 7 Hierarchical Models -- 7.1 Nonparametric Random Effects Distributions -- 7.2 Population PK/PD Models -- 7.3 Hierarchical Models of RPMs -- 7.3.1 Finite Mixtures of Random Probability Measures -- 7.3.2 Dependent Random Probability Measures -- 7.3.3 Classification -- 7.4 Hierarchical, Nested and Enriched DP -- References -- 8 Clustering and Feature Allocation -- 8.1 Random Partitions and Feature Allocations -- 8.2 Polya Urn and Model Based Clustering -- 8.3 Product Partition Models (PPMs) -- 8.3.1 Definition -- 8.3.2 Posterior Simulation -- 8.4 Clustering and Regression -- 8.4.1 The PPMx Model -- 8.4.2 PPMx with Variable Selection -- 8.4.3 Example 26: A PPMx Model for Outlier Detection -- 8.5 Feature Allocation Models -- 8.5.1 Feature Allocation
  • 8.5.2 Indian Buffet Process -- 8.5.3 Approximate Posterior Inference with MAD Bayes -- MAD Bayes Posterior Maximization -- 8.6 Nested Clustering Models -- References -- 9 Other Inference Problems and Conclusion -- References -- A DPpackage -- A.1 Overview -- A.2 An Example -- A.2.1 The Models -- A.2.2 Example 29: Simulated Data -- References -- List of Examples -- Index
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unknown
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{'f': 'http://opac.lib.rpi.edu/record=b4383327'}
Extent
1 online resource (203 pages)
Form of item
online
Isbn
9783319189680
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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