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Bayesian Inference With Inla

Author : Virgilio Gomez-Rubio
ISBN : 9781351707190
Genre : Mathematics
File Size : 78. 36 MB
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The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.

Spatial And Spatio Temporal Bayesian Models With R Inla

Author : Marta Blangiardo
ISBN : 9781118326558
Genre : Mathematics
File Size : 65. 46 MB
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Spatial and Spatio–Temporal Bayesian Models with R–INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio­–temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R–INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations

Bayesian Regression Modeling With Inla

Author : Xiaofeng Wang
ISBN : 9781351165754
Genre : Mathematics
File Size : 60. 63 MB
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INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work. Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic. Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York. Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.

Complex Data Modeling And Computationally Intensive Statistical Methods

Author : Pietro Mantovan
ISBN : 8847013860
Genre : Computers
File Size : 63. 68 MB
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Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.

Complex Models And Computational Methods In Statistics

Author : Matteo Grigoletto
ISBN : 9788847028715
Genre : Mathematics
File Size : 87. 95 MB
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The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.

Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R And Inla

Author : Elias T. Krainski
ISBN : 9780429629853
Genre : Mathematics
File Size : 47. 15 MB
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Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Bayesian Nonparametric Inference Of Effective Population Size Trajectories From Genomic Data

Author : Julia Adela Palacios Roman
ISBN : OCLC:877836347
Genre : Bayesian statistical decision theory
File Size : 54. 71 MB
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Phylodynamics is an area at the intersection of phylogenetics and population genetics that aims to reconstruct population size trajectories from genetic data. Phylodynamic methods rely on a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. The shape of a genealogy is influenced by the effective population size trajectory and, under the coalescent framework, the times at which genealogical lineages coalesce contain information about population size dynamics. I show that these coalescent times can be viewed as realization of a point process and that estimation of population size trajectories is equivalent to estimating a conditional intensity of the coalescent point process. This thesis presents a Gaussian process-based Bayesian nonparametric approach to estimate effective population size trajectories. First, I summarize and discuss current approaches to statistical inference in phylodynamics. Next, I demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics when the genealogy is assumed fixed. I compare our Gaussian process (GP) approach to one of the state of the art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Next, I show that when a representative genealogy is available, perhaps estimated using one of the phylogenetic reconstruction methods, we can replace Markov chain Monte Carlo (MCMC) methods to perform inference by integrated nested Laplace approximation (INLA). This approximation, actively used in spatial statistics, results in recovery of population size trajectories that is much faster than current MCMC based methods. However, the INLA algorithm cannot be generalized to a more realistic setting, where one starts with molecular data instead of a genealogy. Therefore, I return to MCMC to extend the GP approach to infer population size trajectories from molecular data directly. I test the GP-based method on simulated and real data. For real data, I estimate effective number of infected individuals with Hepatits C virus in Egypt from 1700 to 1993, the effective number of individuals infected with human influenza A virus in New York between 2000 and 2005 and effective number of Bisons across Beringia from present time to 100,000 years ago.

Statistical Analysis Of Next Generation Sequencing Data

Author : Somnath Datta
ISBN : 9783319072128
Genre : Medical
File Size : 85. 36 MB
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Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.

Computational Bayesian Statistics

Author : M. Antónia Amaral Turkman
ISBN : 9781108481038
Genre : Business & Economics
File Size : 63. 97 MB
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This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Bayesian Disease Mapping

Author : Andrew B. Lawson
ISBN : 9781466504813
Genre : Mathematics
File Size : 56. 1 MB
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Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

The Hunt Of Cancer Genes Statistical Inference Of Cancer Risk And Driver Genes Using Next Generation Sequencuing Data

Author : Hana Sušak
ISBN : OCLC:1151084311
Genre :
File Size : 52. 51 MB
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International cancer sequencing projects have generated comprehensive catalogs of alterations found in tumor genomes, as well as germline variant data for thousands of individuals. In this thesis, we describe two statistical methods exploiting these rich datasets in order to better understand tumor initiation, tumor progression and the contribution of genetic variants to the lifetime risk of developing cancer. The first method, a Bayesian inference model named cDriver, utilizes multiple signatures of positive selection acting on tumor genomes to predict cancer driver genes. Cancer cell fraction is introduced as a novel signature of positive selection on a cellular level, based on the hypothesis that cells obtaining additional advantageous driver mutations will undergo rapid proliferation and clonal expansion. We benchmarked cDriver against state of the art driver prediction methods on three cancer datasets demonstrating equal or better performance than the best competing tool. The second method, termed REWAS is a comprehensive framework for rare-variant association studies (RVAS) aiming at improving identification of cancer predisposition genes. Nonetheless, REWAS is readily applicable to any case-control study of complex diseases. Besides integrating well-established RVAS methods, we developed a novel Bayesian inference RVAS method (BATI) based on Integrated Nested Laplace Approximation (INLA). We demonstrate that BATI outperforms other methods on realistic simulated datasets, especially when meaningful biological context (e.g. functional impact of variants) is available or when risk variants in sum explain low phenotypic variance. Both methods developed during my thesis have the potential to facilitate personalized medicine and oncology through identification of novel therapeutic targets and identification of genetic predisposition facilitating prevention and early diagnosis of cancer.

Government Reports Announcements Index

Author :
ISBN : UOM:39015034724552
Genre : Science
File Size : 58. 58 MB
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Forthcoming Books

Author : Rose Arny
ISBN : UOM:39015038891522
Genre : American literature
File Size : 77. 67 MB
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