Bayesian Nonparametric Inference For Stochastic Epidemic Models
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Author | : Xiaoguang Xu |
Publisher | : |
Total Pages | : 0 |
Release | : 2015 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Bayesian Nonparametric Inference for Stochastic Epidemic Models Book in PDF, Epub and Kindle
Author | : Rowland G. Seymour |
Publisher | : |
Total Pages | : |
Release | : 2020 |
Genre | : |
ISBN | : |
Download Bayesian Nonparametric Methods for Individual-level Stochastic Epidemic Models Book in PDF, Epub and Kindle
Author | : Tom Britton |
Publisher | : Springer Nature |
Total Pages | : 474 |
Release | : 2019-11-30 |
Genre | : Mathematics |
ISBN | : 3030309002 |
Download Stochastic Epidemic Models with Inference Book in PDF, Epub and Kindle
Focussing 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 | : |
Total Pages | : 222 |
Release | : 2005 |
Genre | : |
ISBN | : |
Download Bayesian Inference for Stochastic Epidemic Models Book in PDF, Epub and Kindle
Author | : Nikolaos Demiris |
Publisher | : |
Total Pages | : |
Release | : 2004 |
Genre | : |
ISBN | : |
Download Bayesian Inference for Stochastic Epidemic Models Using Markov Chain Monte Carlo Methods Book in PDF, Epub and Kindle
Author | : Tom Britton |
Publisher | : |
Total Pages | : 477 |
Release | : 2019 |
Genre | : Biomathematics |
ISBN | : 9783030309015 |
Download Stochastic Epidemic Models with Inference Book in PDF, Epub and Kindle
Focussing 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 | : |
Total Pages | : |
Release | : 2020 |
Genre | : |
ISBN | : |
Download Topics in Bayesian Inference and Model Assessment for Partially Observed Stochastic Epidemic Models Book in PDF, Epub and Kindle
Author | : Sneh Gulati |
Publisher | : Springer Science & Business Media |
Total Pages | : 123 |
Release | : 2013-03-14 |
Genre | : Mathematics |
ISBN | : 0387215492 |
Download Parametric and Nonparametric Inference from Record-Breaking Data Book in PDF, Epub and Kindle
By 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 |
Total Pages | : 263 |
Release | : 2023-08-23 |
Genre | : Mathematics |
ISBN | : 1000927717 |
Download Bayesian Nonparametrics for Causal Inference and Missing Data Book in PDF, Epub and Kindle
Bayesian 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 | : Hakan Andersson |
Publisher | : Springer Science & Business Media |
Total Pages | : 140 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461211581 |
Download Stochastic Epidemic Models and Their Statistical Analysis Book in PDF, Epub and Kindle
The present lecture notes describe stochastic epidemic models and methods for their statistical analysis. Our aim is to present ideas for such models, and methods for their analysis; along the way we make practical use of several probabilistic and statistical techniques. This will be done without focusing on any specific disease, and instead rigorously analyzing rather simple models. The reader of these lecture notes could thus have a two-fold purpose in mind: to learn about epidemic models and their statistical analysis, and/or to learn and apply techniques in probability and statistics. The lecture notes require an early graduate level knowledge of probability and They introduce several techniques which might be new to students, but our statistics. intention is to present these keeping the technical level at a minlmum. Techniques that are explained and applied in the lecture notes are, for example: coupling, diffusion approximation, random graphs, likelihood theory for counting processes, martingales, the EM-algorithm and MCMC methods. The aim is to introduce and apply these techniques, thus hopefully motivating their further theoretical treatment. A few sections, mainly in Chapter 5, assume some knowledge of weak convergence; we hope that readers not familiar with this theory can understand the these parts at a heuristic level. The text is divided into two distinct but related parts: modelling and estimation.