Bayesian Method 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 : 50,5 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 : Lyle D. Broemeling
Publisher : CRC Press
Page : 468 pages
File Size : 47,6 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 : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 663 pages
File Size : 40,5 Mb
Release : 2013-11-27
Category : Mathematics
ISBN : 9781439898208

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Bayesian Data Analysis 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 AnalysisNow 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

Author : Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
Publisher : Elsevier
Page : 404 pages
File Size : 48,7 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 : Peter Congdon
Publisher : John Wiley & Sons
Page : 464 pages
File Size : 55,6 Mb
Release : 2014-05-23
Category : Mathematics
ISBN : 9781118895054

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Applied Bayesian Modelling by Peter Congdon Pdf

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

Author : John Geweke,Gary Koop,Herman van Dijk
Publisher : Oxford University Press
Page : 576 pages
File Size : 51,6 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 : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 48,8 Mb
Release : 2024-06-02
Category : Electronic
ISBN : 9789814465441

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Machine Interpretation of Patterns by Anonim Pdf

Author : Bogdan Gabrys
Publisher : Springer
Page : 1301 pages
File Size : 41,9 Mb
Release : 2006-10-18
Category : Computers
ISBN : 9783540465447

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Knowledge-Based Intelligent Information and Engineering Systems by Bogdan Gabrys Pdf

The three volume set LNAI 4251, LNAI 4252, and LNAI 4253 constitutes the refereed proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006, held in Bournemouth, UK, in October 2006. The 480 revised papers presented were carefully reviewed and selected from about 1400 submissions. The papers present a wealth of original research results from the field of intelligent information processing.

Author : Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publisher : CRC Press
Page : 165 pages
File Size : 44,7 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 : Anonim
Publisher : Elsevier
Page : 1062 pages
File Size : 54,9 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

Author : Stephane Heritier,Eva Cantoni,Samuel Copt,Maria-Pia Victoria-Feser
Publisher : John Wiley & Sons
Page : 292 pages
File Size : 42,6 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 : Andrew B. Lawson
Publisher : John Wiley & Sons
Page : 302 pages
File Size : 40,5 Mb
Release : 2013-07-08
Category : Medical
ISBN : 9781118723173

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Statistical Methods in Spatial Epidemiology by Andrew B. Lawson Pdf

Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

Author : H B Mitchell
Publisher : Springer Science & Business Media
Page : 349 pages
File Size : 55,8 Mb
Release : 2012-02-09
Category : Technology & Engineering
ISBN : 9783642272226

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Data Fusion: Concepts and Ideas by H B Mitchell Pdf

This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by Springer-Verlag in 2007. The main changes in the new book are: New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages longer than the original book. Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text. Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended. Although conceptually simple, the study of mult-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often, the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In contrast, in this book the processes are unified by using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident. The book is illustrated with many real-life examples taken from a diverse range of applications and contains an extensive list of modern references.

Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1102 pages
File Size : 53,8 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 : Christine Sinoquet,Raphaël Mourad
Publisher : Oxford University Press, USA
Page : 483 pages
File Size : 43,5 Mb
Release : 2014
Category : Mathematics
ISBN : 9780198709022

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Probabilistic Graphical Models for Genetics, Genomics and Postgenomics by Christine Sinoquet,Raphaël Mourad Pdf

At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.