Bayesian Nonparametric Inference for Stochastic Epidemic Models
Author: Xiaoguang Xu
Publisher:
Published: 2015
Total Pages: 0
ISBN-13:
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Author: Xiaoguang Xu
Publisher:
Published: 2015
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKAuthor: Rowland G. Seymour
Publisher:
Published: 2020
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Tom Britton
Publisher: Springer Nature
Published: 2019-11-30
Total Pages: 474
ISBN-13: 3030309002
DOWNLOAD EBOOKFocussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.
Author: Philip Robert Giles
Publisher:
Published: 2005
Total Pages: 222
ISBN-13:
DOWNLOAD EBOOKAuthor: Nikolaos Demiris
Publisher:
Published: 2004
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Tom Britton
Publisher:
Published: 2019
Total Pages: 477
ISBN-13: 9783030309015
DOWNLOAD EBOOKFocussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5-16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.
Author: Georgios Aristotelous
Publisher:
Published: 2020
Total Pages:
ISBN-13:
DOWNLOAD EBOOKAuthor: Sneh Gulati
Publisher: Springer Science & Business Media
Published: 2013-03-14
Total Pages: 123
ISBN-13: 0387215492
DOWNLOAD EBOOKBy providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail. Its main purpose is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.
Author: Michael J. Daniels
Publisher: CRC Press
Published: 2023-08-23
Total Pages: 263
ISBN-13: 1000927717
DOWNLOAD EBOOKBayesian Nonparametrics for Causal Inference and Missing Data 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. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then 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 non-ignorable 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.
Author: Stephen Walker
Publisher:
Published: 1997
Total Pages:
ISBN-13:
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