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
Page : 302 pages
File Size : 51,9 Mb
Release : 2002-05-06
Category : Mathematics
ISBN : 0471490369

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Bayesian Methods for Nonlinear Classification and Regression by David G. T. Denison,Christopher C. Holmes,Bani K. Mallick,Adrian F. M. Smith Pdf

Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.

Author : Christopher Charles De Lance Holmes
Publisher : Unknown
Page : 0 pages
File Size : 41,8 Mb
Release : 2001
Category : Electronic
ISBN : OCLC:59365270

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Bayesian Method for Nonlinear Classification and Regression by Christopher Charles De Lance Holmes Pdf

Author : Simon Jackman
Publisher : John Wiley & Sons
Page : 598 pages
File Size : 40,6 Mb
Release : 2009-10-27
Category : Mathematics
ISBN : 0470686634

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Bayesian Analysis for the Social Sciences by Simon Jackman Pdf

Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 468 pages
File Size : 55,8 Mb
Release : 2013-08-13
Category : Mathematics
ISBN : 9781466564978

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Bayesian Methods in Epidemiology by Lyle D. Broemeling Pdf

Written by a biostatistics expert with over 20 years of experience in the field, Bayesian Methods in Epidemiology presents statistical methods used in epidemiology from a Bayesian viewpoint. It employs the software package WinBUGS to carry out the analyses and offers the code in the text and for download online. The book examines study designs that investigate the association between exposure to risk factors and the occurrence of disease. It covers introductory adjustment techniques to compare mortality between states and regression methods to study the association between various risk factors and disease, including logistic regression, simple and multiple linear regression, categorical/ordinal regression, and nonlinear models. The text also introduces a Bayesian approach for the estimation of survival by life tables and illustrates other approaches to estimate survival, including a parametric model based on the Weibull distribution and the Cox proportional hazards (nonparametric) model. Using Bayesian methods to estimate the lead time of the modality, the author explains how to screen for a disease among individuals that do not exhibit any symptoms of the disease. With many examples and end-of-chapter exercises, this book is the first to introduce epidemiology from a Bayesian perspective. It shows epidemiologists how these Bayesian models and techniques are useful in studying the association between disease and exposure to risk factors.

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 568 pages
File Size : 53,9 Mb
Release : 2015-08-04
Category : Mathematics
ISBN : 9781482248203

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Bayesian Methods for Repeated Measures by Lyle D. Broemeling Pdf

Analyze Repeated Measures Studies Using Bayesian TechniquesGoing beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas,

Author : David Insua,Fabrizio Ruggeri,Mike Wiper
Publisher : John Wiley & Sons
Page : 315 pages
File Size : 42,7 Mb
Release : 2012-04-02
Category : Mathematics
ISBN : 9781118304037

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Bayesian Analysis of Stochastic Process Models by David Insua,Fabrizio Ruggeri,Mike Wiper Pdf

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

Author : Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
Publisher : Elsevier
Page : 404 pages
File Size : 40,9 Mb
Release : 2016-09-10
Category : Mathematics
ISBN : 9780444637512

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Cognitive Computing: Theory and Applications by Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao Pdf

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 : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 675 pages
File Size : 42,9 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781439898208

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Bayesian Data Analysis, Third Edition by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 47,6 Mb
Release : 2024-05-10
Category : Electronic
ISBN : 9789814465441

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Machine Interpretation of Patterns by Anonim Pdf

Author : Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 48,6 Mb
Release : 2009-05-11
Category : Medical
ISBN : 047074054X

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Robust Methods in Biostatistics by Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser Pdf

Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Author : John Geweke,Gary Koop,Herman van Dijk
Publisher : Oxford University Press
Page : 576 pages
File Size : 52,7 Mb
Release : 2011-09-29
Category : Business & Economics
ISBN : 9780191618260

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The Oxford Handbook of Bayesian Econometrics by John Geweke,Gary Koop,Herman van Dijk Pdf

Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.

Author : Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publisher : CRC Press
Page : 165 pages
File Size : 40,5 Mb
Release : 2022-04-14
Category : Business & Economics
ISBN : 9781000569599

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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications by Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose Pdf

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

Author : Elena Kulinskaya,Stephan Morgenthaler,Robert G. Staudte
Publisher : John Wiley & Sons
Page : 282 pages
File Size : 47,5 Mb
Release : 2008-04-15
Category : Mathematics
ISBN : 0470985526

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Meta Analysis by Elena Kulinskaya,Stephan Morgenthaler,Robert G. Staudte Pdf

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1102 pages
File Size : 54,7 Mb
Release : 2012-08-24
Category : Computers
ISBN : 9780262018029

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Machine Learning by Kevin P. Murphy Pdf

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Author : Anonim
Publisher : Elsevier
Page : 1062 pages
File Size : 55,6 Mb
Release : 2005-11-29
Category : Mathematics
ISBN : 9780080461175

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Bayesian Thinking, Modeling and Computation by Anonim Pdf

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics