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• Author : Yanan Fan,David Nott,Mike S. Smith,Jean-Luc Dortet-Bernadet
• Publisher :Unknown
• Release Date :2019-10-30
• Total pages :302
• ISBN : 9780128158630

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Summary : 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’

• Author : José M. Bernardo,James O. Berger,A. P. Dawid,Adrian F. M. Smith
• Publisher :Unknown
• Release Date :1999-08-12
• Total pages :867
• ISBN : 0198504853

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Summary : 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.

• Author : Anthony O' Hagan,Mike West
• Publisher :Unknown
• Release Date :2010-03-18
• Total pages :924
• ISBN : 9780191613890

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Summary : Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.

• Author : Peter D. Congdon
• Publisher :Unknown
• Release Date :2019-09-16
• Total pages :580
• ISBN : 9781498785914

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Summary : 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

• Author : Thomas Kneib,Gerhard Tutz
• Publisher :Unknown
• Release Date :2010-01-12
• Total pages :472
• ISBN : 9783790824131

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Summary : 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.

• Author : Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
• Publisher :Unknown
• Release Date :2016-09-10
• Total pages :404
• ISBN : 9780444637512

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Summary : 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

• Author : David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith
• Publisher :Unknown
• Release Date :2002-05-06
• Total pages :296
• ISBN : 0471490369

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Summary : 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.

• Author : José M. Bernardo,M. J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman
• Publisher :Unknown
• Release Date :2011-10-06
• Total pages :706
• ISBN : 9780199694587

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Summary : 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.

• Author : Anonim
• Publisher :Unknown
• Release Date :2008
• Total pages :229
• ISBN : UCSD:31822036057495

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Summary :

• Author : Anonim
• Publisher :Unknown
• Release Date :2013-05-01
• Total pages :770
• ISBN : 9781490111421

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Summary : 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/.

• Author : Anonim
• Publisher :Unknown
• Release Date :2001
• Total pages :229
• ISBN : UCBK:C078266509

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Summary :

• Author : Kim-Anh Do,Peter Müller,Marina Vannucci
• Publisher :Unknown
• Release Date :2006-07-24
• Total pages :437
• ISBN : 9780521860925

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

• Author : Vanda Inácio de Carvalho,Miguel Brás de Carvalho,Wesley O. Johnson,Adam Branscum
• Publisher :Unknown
• Release Date :2016-05-15
• Total pages :250
• ISBN : 1466580399

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Summary : 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.

• Author : Anonim
• Publisher :Unknown
• Release Date :2000
• Total pages :229
• ISBN : UVA:X006093804

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Summary :

• Author : Anonim
• Publisher :Unknown
• Release Date :1998
• Total pages :229
• ISBN : UCAL:B4595241

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Summary :

• Author : Christos H. Skiadas,James R. Bozeman
• Publisher :Unknown
• Release Date :2019-03-04
• Total pages :286
• ISBN : 9781119597575

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Summary : This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

• Author : Eric Parent,Etienne Rivot
• Publisher :Unknown
• Release Date :2012-08-21
• Total pages :427
• ISBN : 9781584889199

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Summary : 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.

• Author : Franzi Korner-Nievergelt,Tobias Roth,Stefanie von Felten,Jérôme Guélat,Bettina Almasi,Pius Korner-Nievergelt
• Publisher :Unknown
• Release Date :2015-04-04
• Total pages :328
• ISBN : 9780128016787

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Summary : Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco

• Author : Xiaofeng Wang,Yu Yue Ryan,Julian J. Faraway
• Publisher :Unknown
• Release Date :2018-01-29
• Total pages :312
• ISBN : 9781351165747

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Summary : This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated nested Laplace approximations (INLA) and real data analysis using R. The INLA method directly computes very accurate approximations to the posterior marginal distributions and is a promising alternative to Markov chain Monte Carlo (MCMC) algorithms, which come with a range of issues that impede practical use of Bayesian models.

• Author : Abhishek Bishoyi
• Publisher :Unknown
• Release Date :2017
• Total pages :229
• ISBN : OCLC:1196371746

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Summary : This dissertation aims at introducing Gaussian process priors on the regression to capture features of dataset more adequately. Three different types of problems occur often in the regression. 1) For the dataset with missing covariates in the semiparametric regression, we utilize Gaussian process priors on the nonparametric component of the regression function to perform imputations of missing covariates. For the Bayesian inference of parameters, we specify objective priors on the Gaussian process parameters.Posteriorpropriety of the model under the objective priors is also demonstrated. 2) For modeling binary and ordinal data, we proposed a flexible nonparametric regression model that combines flexible power link function with a Gaussian process prior on the latent regression function. We develop an efficient sampling algorithm for posterior inference and prove the posterior consistency of the proposed model. 3) In the high dimensional dataset, the estimation of regression coefficients especially when the covariates are highly multicollinear is very challenging. Therefore, we develop a model by using structured spike an slab prior on regression coefficients. Prior information of similarity between covariates can be encoded into the covariance structure of Gaussian process which can be used to induce sparsity. Hyperparameters of the Gaussian process can be used to control different sparsity pattern. The superiority of the proposed model is demonstrated using various simulation studies and real data examples.

• Author : Matt Taddy
• Publisher :Unknown
• Release Date :2019-08-23
• Total pages :384
• ISBN : 9781260452785

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Summary : Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: •Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling•Understand how use ML tools in real world business problems, where causation matters more that correlation•Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science.