Bayesian Nonparametrics
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Bayesian Nonparametrics
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Author : J.K. Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-11
Bayesian Nonparametrics written by J.K. Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-11 with Mathematics categories.
This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Bayesian Nonparametrics
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Author : Nils Lid Hjort
language : en
Publisher: Cambridge University Press
Release Date : 2010-04-12
Bayesian Nonparametrics written by Nils Lid Hjort and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-12 with Mathematics categories.
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Bayesian Nonparametric Data Analysis
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Author : Peter Müller
language : en
Publisher: Springer
Release Date : 2015-06-17
Bayesian Nonparametric Data Analysis written by Peter Müller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-17 with Mathematics categories.
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.
Fundamentals Of Nonparametric Bayesian Inference
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Author : Subhashis Ghosal
language : en
Publisher: Cambridge University Press
Release Date : 2017-06-26
Fundamentals Of Nonparametric Bayesian Inference written by Subhashis Ghosal and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-26 with Business & Economics categories.
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
Bayesian Nonparametrics Via Neural Networks
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Author : Herbert K. H. Lee
language : en
Publisher: SIAM
Release Date : 2004-01-01
Bayesian Nonparametrics Via Neural Networks written by Herbert K. H. Lee and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-01 with Mathematics categories.
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Nonparametric Bayesian Inference In Biostatistics
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Author : Riten Mitra
language : en
Publisher: Springer
Release Date : 2015-07-25
Nonparametric Bayesian Inference In Biostatistics written by Riten Mitra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-25 with Medical categories.
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.
Bayesian Nonparametrics For Causal Inference And Missing Data
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Author : Michael J. Daniels
language : en
Publisher: CRC Press
Release Date : 2023-08-23
Bayesian Nonparametrics For Causal Inference And Missing Data written by Michael J. Daniels and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-23 with categories.
Bayesian Nonparametric Methods for Missing Data and Causal Inference provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. The BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification, unlike parametric methods. The overall strategy is to first specify BNP models for observed data and second to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data, and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features: * Thorough discussion of both BNP and its interplay with causal inference and missing data * How to use BNP and g-computation for causal inference and nonignorable missingness * How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions * Detailed case studies illustrating the application of BNP methods to causal inference and missing data * R-code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically-sophisticated epidemiologists and medical researchers.
Practical Nonparametric And Semiparametric Bayesian Statistics
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Author : Dipak D. Dey
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Practical Nonparametric And Semiparametric Bayesian Statistics written by Dipak D. Dey and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.
Bayesian Nonparametric Data Analysis
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Author : Peter Muller
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-07-26
Bayesian Nonparametric Data Analysis written by Peter Muller and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-26 with categories.
Bayesian Nonparametric Data AnalysisBy Peter M�ller
Nonparametric Bayesian Inference
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Author : Peter Müller
language : en
Publisher:
Release Date : 2013
Nonparametric Bayesian Inference written by Peter Müller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Bayesian statistical decision theory categories.