Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Author: Jean-Michel Marin,Christian Robert

Publisher: Springer Science & Business Media

ISBN: 0387389830

Category: Mathematics

Page: 258

View: 4264

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This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book.
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Introduction to Bayesian Econometrics

Author: Edward Greenberg

Publisher: Cambridge University Press

ISBN: 1107015316

Category: Business & Economics

Page: 249

View: 2428

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Introduces the increasingly popular Bayesian approach to statistics to graduates and advanced undergraduates. In contrast to the long-standing frequentist approach to statistics, the Bayesian approach makes explicit use of prior information and is based on the subjective view of probability. Bayesian econometrics takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters. A distinguishing feature of this book is its emphasis on classical and Markov chain Monte Carlo (MCMC) methods of simulation. The book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. These include the linear regression model and extensions to Tobit, probit, and logit models; time series models; and models involving endogenous variables.
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Multiscale Modeling

A Bayesian Perspective

Author: Marco A.R. Ferreira,Herbert K.H. Lee

Publisher: Springer Science & Business Media

ISBN: 0387708987

Category: Mathematics

Page: 245

View: 5113

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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.
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The Bayesian Choice

From Decision-Theoretic Foundations to Computational Implementation

Author: Christian Robert

Publisher: Springer Science & Business Media

ISBN: 0387715991

Category: Mathematics

Page: 606

View: 8649

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This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.
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AMSTAT News

Author: American Statistical Association

Publisher: N.A

ISBN: N.A

Category: Statistics

Page: N.A

View: 6551

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Fundamentals of Predictive Text Mining

Author: Sholom M. Weiss,Nitin Indurkhya,Tong Zhang

Publisher: Springer

ISBN: 1447167503

Category: Computers

Page: 239

View: 8799

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This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
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Practical Text Mining with Perl

Author: Roger Bilisoly

Publisher: John Wiley & Sons Incorporated

ISBN: N.A

Category: Computers

Page: 295

View: 8036

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This text shows the reader how to actually perform text mining. It emphasises practical examples using open source tools applied to freely available data over the Internet and provides programming code and program output for practical examples of analysing text.
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