January 21, 2021

Download Ebook Free Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling

Flexible Bayesian Regression Modelling
Author : Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
Publisher : Academic Press
Release Date : 2019-10-30
Category : Business & Economics
Total pages :302
GET BOOK

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine. Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitioners Focuses on approaches offering both superior power and methodological flexibility Supplemented with instructive and relevant R programs within the text Covers linear regression, nonlinear regression and quantile regression techniques Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis ‘in the wild’

Bayesian Statistics 6

Bayesian Statistics 6
Author : José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith
Publisher : Oxford University Press
Release Date : 1999-08-12
Category : Business & Economics
Total pages :867
GET BOOK

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.

Bayesian Hierarchical Models

Bayesian Hierarchical Models
Author : Peter D. Congdon
Publisher : CRC Press
Release Date : 2019-09-16
Category : Mathematics
Total pages :580
GET BOOK

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Statistical Modelling and Regression Structures

Statistical Modelling and Regression Structures
Author : Thomas Kneib,Gerhard Tutz
Publisher : Springer Science & Business Media
Release Date : 2010-01-12
Category : Mathematics
Total pages :472
GET BOOK

The contributions collected in this book have been written by well-known statisticians to acknowledge Ludwig Fahrmeir's far-reaching impact on Statistics as a science, while celebrating his 65th birthday. The contributions cover broad areas of contemporary statistical model building, including semiparametric and geoadditive regression, Bayesian inference in complex regression models, time series modelling, statistical regularization, graphical models and stochastic volatility models.

Cognitive Computing: Theory and Applications

Cognitive Computing: Theory and Applications
Author : Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
Publisher : Elsevier
Release Date : 2016-09-10
Category : Mathematics
Total pages :404
GET BOOK

Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowned experts in their respective areas

Bayesian Methods for Nonlinear Classification and Regression

Bayesian Methods for Nonlinear Classification and Regression
Author : David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith
Publisher : John Wiley & Sons
Release Date : 2002-05-06
Category : Mathematics
Total pages :296
GET BOOK

Nonlinear Bayesian modelling is a relatively new field, but one that has seen a recent explosion of interest. Nonlinear models offer more flexibility than those with linear assumptions, and their implementation has now become much easier due to increases in computational power. Bayesian methods allow for the incorporation of prior information, allowing the user to make coherent inference. Bayesian Methods for Nonlinear Classification and Regression is the first book to bring together, in a consistent statistical framework, the ideas of nonlinear modelling and Bayesian methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches. * Demonstrates how Bayesian ideas can be used to improve existing statistical methods. * Includes coverage of Bayesian additive models, decision trees, nearest-neighbour, wavelets, regression splines, and neural networks. * Emphasis is placed on sound implementation of nonlinear models. * Discusses medical, spatial, and economic applications. * Includes problems at the end of most of the chapters. * Supported by a web site featuring implementation code and data sets. Primarily of interest to researchers of nonlinear statistical modelling, the book will also be suitable for graduate students of statistics. The book will benefit researchers involved inregression and classification modelling from electrical engineering, economics, machine learning and computer science.

Bayesian Statistics 9

Bayesian Statistics 9
Author : José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman
Publisher : Oxford University Press
Release Date : 2011-10-06
Category : Mathematics
Total pages :706
GET BOOK

Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.

Journal of the American Statistical Association

Journal of the American Statistical Association
Author : Anonim
Publisher : Unknown
Release Date : 2008
Category : Statistics
Total pages :129
GET BOOK

Issues in Calculus, Mathematical Analysis, and Nonlinear Research: 2013 Edition

Issues in Calculus, Mathematical Analysis, and Nonlinear Research: 2013 Edition
Author : Anonim
Publisher : ScholarlyEditions
Release Date : 2013-05-01
Category : Mathematics
Total pages :770
GET BOOK

Issues in Calculus, Mathematical Analysis, and Nonlinear Research: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Mathematical Analysis. The editors have built Issues in Calculus, Mathematical Analysis, and Nonlinear Research: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Mathematical Analysis in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Calculus, Mathematical Analysis, and Nonlinear Research: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Statistical Theory and Method Abstracts

Statistical Theory and Method Abstracts
Author : Anonim
Publisher : Unknown
Release Date : 2001
Category : Statistics
Total pages :129
GET BOOK

Bayesian Inference for Gene Expression and Proteomics

Bayesian Inference for Gene Expression and Proteomics
Author : Kim-Anh Do,Peter Müller,Marina Vannucci
Publisher : Cambridge University Press
Release Date : 2006-07-24
Category : Mathematics
Total pages :437
GET BOOK

Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.

Flexible Bayesian Models for Medical Diagnostic Data

Flexible Bayesian Models for Medical Diagnostic Data
Author : Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum
Publisher : Chapman and Hall/CRC
Release Date : 2016-05-15
Category : Mathematics
Total pages :250
GET BOOK

Offering a detailed and careful explanation of the methods, this book delineates Bayesian non parametric techniques to be used in health care and the statistical evaluation of diagnostic tests to determine accuracy before mass use in practice. Unique to these methods is the incorporation of prior information and elimination of subjective beliefs and asymptotic results. It includes examples such as ROC curves and ROC surfaces estimation, modeling of multivariate diagnostic data, absence of a perfect test, ROC regression methodology, and sample size determination.

Mathematical Reviews

Mathematical Reviews
Author : Anonim
Publisher : Unknown
Release Date : 2000
Category : Mathematics
Total pages :129
GET BOOK

Bulletin

Bulletin
Author : Anonim
Publisher : Unknown
Release Date : 1998
Category :
Total pages :129
GET BOOK

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data
Author : Eric Parent,Etienne Rivot
Publisher : CRC Press
Release Date : 2012-08-21
Category : Mathematics
Total pages :427
GET BOOK

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.