Bayesian Nonparametrics Via Neural Networks

Author : Herbert K. H. Lee
Publisher : SIAM
Page : 106 pages
File Size : 42,5 Mb
Release : 2004-01-01
Category : Mathematics
ISBN : 0898718422

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Bayesian Nonparametrics via Neural Networks by Herbert K. H. Lee Pdf

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Author : Herbert K. H. Lee
Publisher : SIAM
Page : 103 pages
File Size : 48,9 Mb
Release : 2004-06-01
Category : Mathematics
ISBN : 9780898715637

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Bayesian Nonparametrics via Neural Networks by Herbert K. H. Lee Pdf

This is the first book to discuss neural networks in a nonparametric regression and classification context, within the Bayesian paradigm.

Author : Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker
Publisher : Cambridge University Press
Page : 309 pages
File Size : 51,7 Mb
Release : 2010-04-12
Category : Mathematics
ISBN : 9781139484602

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Bayesian Nonparametrics by Nils Lid Hjort,Chris Holmes,Peter Müller,Stephen G. Walker Pdf

Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 286 pages
File Size : 53,9 Mb
Release : 2020-08-25
Category : Business & Economics
ISBN : 9780429775543

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Author : William J. Blackwell,Frederick W. Chen
Publisher : Artech House
Page : 232 pages
File Size : 53,5 Mb
Release : 2009
Category : Computers
ISBN : 9781596933736

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Neural Networks in Atmospheric Remote Sensing by William J. Blackwell,Frederick W. Chen Pdf

This authoritative reference offers you a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. You find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. The book provides clear explanations of the mathematical and physical foundations of remote sensing systems, including radiative transfer and propagation theory, sensor technologies, and inversion and estimation approaches. You discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.

Author : Radford M. Neal
Publisher : Springer
Page : 204 pages
File Size : 45,6 Mb
Release : 1996-08-09
Category : Mathematics
ISBN : 0387947248

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Bayesian Learning for Neural Networks by Radford M. Neal Pdf

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Author : David Insua,Fabrizio Ruggeri,Mike Wiper
Publisher : John Wiley & Sons
Page : 315 pages
File Size : 41,9 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 : Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl
Publisher : John Wiley & Sons
Page : 278 pages
File Size : 52,7 Mb
Release : 2015-11-16
Category : Medical
ISBN : 9783527337248

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Computational Network Theory by Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl Pdf

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Author : Clair L. Alston,Kerrie L. Mengersen,Anthony N. Pettitt
Publisher : John Wiley & Sons
Page : 411 pages
File Size : 50,8 Mb
Release : 2012-10-10
Category : Mathematics
ISBN : 9781118394328

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Case Studies in Bayesian Statistical Modelling and Analysis by Clair L. Alston,Kerrie L. Mengersen,Anthony N. Pettitt Pdf

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.

Author : American Statistical Association
Publisher : Unknown
Page : 536 pages
File Size : 47,7 Mb
Release : 2009
Category : Statistics
ISBN : UOM:39015085193624

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AMSTAT News by American Statistical Association Pdf

Author : Marco A.R. Ferreira,Herbert K.H. Lee
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 52,7 Mb
Release : 2007-07-17
Category : Mathematics
ISBN : 9780387708980

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Multiscale Modeling by Marco A.R. Ferreira,Herbert K.H. Lee Pdf

This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.

Author : Bertrand S. Clarke,Jennifer L. Clarke
Publisher : Cambridge University Press
Page : 657 pages
File Size : 55,8 Mb
Release : 2018-04-12
Category : Business & Economics
ISBN : 9781107028289

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Predictive Statistics by Bertrand S. Clarke,Jennifer L. Clarke Pdf

A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.

Author : Anonim
Publisher : Newnes
Page : 612 pages
File Size : 52,8 Mb
Release : 2012-12-31
Category : Mathematics
ISBN : 9780080930985

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Handbook of Statistics by Anonim Pdf

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics, a series of self-contained reference books. Each volume is devoted to a particular topic in statistics with Volume 28 dealing with bioinformatics. Every chapter is written by prominent workers in the area to which the volume is devoted. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. 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 : Ranajit Chakraborty,C.R. Rao,Pranab K. Sen
Publisher : Newnes
Page : 614 pages
File Size : 55,7 Mb
Release : 2012-10-03
Category : Computers
ISBN : 9780444518750

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Bioinformatics in Human Health and Heredity by Ranajit Chakraborty,C.R. Rao,Pranab K. Sen Pdf

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. A series of handbooks is the only way of presenting the various aspects of statistical methodology, applications and developments. This volume deals with bioinformatics.

Author : Wendell F. Smith
Publisher : SIAM
Page : 386 pages
File Size : 52,6 Mb
Release : 2005-01-01
Category : Science
ISBN : 0898718392

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Experimental Design for Formulation by Wendell F. Smith Pdf

Many products, such as foods, personal-care products, beverages, and cleaning agents, are made by mixing ingredients together. This book describes a systematic methodology for formulating such products so that they perform according to one's goals, providing scientists and engineers with a fast track to the implementation of the methodology. Experimental Design for Formulation contains examples from a wide variety of fields and includes a discussion of how to design experiments for a mixture setting and how to fit and interpret models in a mixture setting. It also introduces process variables, the combining of mixture and nonmixture variables in a designed experiment, and the concept of collinearity and the possible problems that can result from its presence. Experimental Design for Formulation is a useful manual for the formulator and can also be used by a resident statistician to teach an in-house short course. Statistical proofs are largely absent, and the formulas that are presented are included to explain how the various software packages carry out the analysis. Many examples are given of output from statistical software packages, and the proper interpretation of computer output is emphasized. Other topics presented include a discussion of an effect in a mixture setting, the presentation of elementary optimization methods, and multiple-response optimization wherein one seeks to optimize more than one response.