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The Resource Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource)
Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource)
Resource Information
The item Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 Summary
 As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics
 Language
 eng
 Edition
 1st ed. 2015.
 Extent
 XV, 450 p. 96 illus., 47 illus. in color.
 Contents

 Part I Introduction
 Bayesian Nonparametric Models
 Bayesian Nonparametric Biostatistics
 Part II Genomics and Proteomics
 Bayesian Shape Clustering
 Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models
 Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations
 Modeling the Association Between Clusters of SNPs and Disease Responses
 Bayesian Inference on Population Structure: from Parametric to Nonparametric Modeling
 Bayesian Approaches for Large Biological Networks
 Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets
 Part III Survival Analysis
 Markov Processes in Survival Analysis
 Bayesian Spatial Survival Models
 Fully Nonparametric Regression Modelling of Misclassified Censored TimetoEvent Data
 Part IV Random Functions and Response Surfaces
 Neuronal Spike Train Analysis Using Gaussian Process Models
 Bayesian Analysis of Curves Shape Variation through Registration and Regression
 BiomarkerDriven Adaptive Design
 Bayesian Nonparametric Approaches for ROC Curve Inference
 Part V Spatial Data
 Spatial Bayesian Nonparametric Methods
 Spatial Species Sampling and Product Partition Models
 Spatial Boundary Detection for Areal Counts
 A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
 Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials
 Isbn
 9783319195186
 Label
 Nonparametric Bayesian Inference in Biostatistics
 Title
 Nonparametric Bayesian Inference in Biostatistics
 Statement of responsibility
 edited by Riten Mitra, Peter Müller
 Language
 eng
 Summary
 As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics
 Image bit depth
 0
 LC call number
 QA276280
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName

 Mitra, Riten.
 Müller, Peter
 SpringerLink
 Series statement
 Frontiers in Probability and the Statistical Sciences
 http://library.link/vocab/subjectName

 Statistics
 Statistical methods
 Mathematical statistics
 Statistics
 Statistics for Life Sciences, Medicine, Health Sciences
 Biostatistics
 Statistical Theory and Methods
 Label
 Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Part I Introduction  Bayesian Nonparametric Models  Bayesian Nonparametric Biostatistics  Part II Genomics and Proteomics  Bayesian Shape Clustering  Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models  Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations  Modeling the Association Between Clusters of SNPs and Disease Responses  Bayesian Inference on Population Structure: from Parametric to Nonparametric Modeling  Bayesian Approaches for Large Biological Networks  Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets  Part III Survival Analysis  Markov Processes in Survival Analysis  Bayesian Spatial Survival Models  Fully Nonparametric Regression Modelling of Misclassified Censored TimetoEvent Data  Part IV Random Functions and Response Surfaces  Neuronal Spike Train Analysis Using Gaussian Process Models  Bayesian Analysis of Curves Shape Variation through Registration and Regression  BiomarkerDriven Adaptive Design  Bayesian Nonparametric Approaches for ROC Curve Inference  Part V Spatial Data  Spatial Bayesian Nonparametric Methods  Spatial Species Sampling and Product Partition Models  Spatial Boundary Detection for Areal Counts  A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs  Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials
 Dimensions
 unknown
 Edition
 1st ed. 2015.
 Extent
 XV, 450 p. 96 illus., 47 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319195186
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319195186
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319195186
 Label
 Nonparametric Bayesian Inference in Biostatistics, edited by Riten Mitra, Peter Müller, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 Part I Introduction  Bayesian Nonparametric Models  Bayesian Nonparametric Biostatistics  Part II Genomics and Proteomics  Bayesian Shape Clustering  Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models  Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations  Modeling the Association Between Clusters of SNPs and Disease Responses  Bayesian Inference on Population Structure: from Parametric to Nonparametric Modeling  Bayesian Approaches for Large Biological Networks  Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets  Part III Survival Analysis  Markov Processes in Survival Analysis  Bayesian Spatial Survival Models  Fully Nonparametric Regression Modelling of Misclassified Censored TimetoEvent Data  Part IV Random Functions and Response Surfaces  Neuronal Spike Train Analysis Using Gaussian Process Models  Bayesian Analysis of Curves Shape Variation through Registration and Regression  BiomarkerDriven Adaptive Design  Bayesian Nonparametric Approaches for ROC Curve Inference  Part V Spatial Data  Spatial Bayesian Nonparametric Methods  Spatial Species Sampling and Product Partition Models  Spatial Boundary Detection for Areal Counts  A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs  Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials
 Dimensions
 unknown
 Edition
 1st ed. 2015.
 Extent
 XV, 450 p. 96 illus., 47 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783319195186
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783319195186
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783319195186
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