Fundamentals of Nonparametric Bayesian Inference

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Author: Subhashis Ghosal,Aad van der Vaart

Publisher: Cambridge University Press

ISBN: 0521878268

Category: Business & Economics

Page: 670

View: 7396

Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.

2017 MATRIX Annals

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Author: David R. Wood,Jan de Gier,Cheryl E. Praeger,Terence Tao

Publisher: Springer

ISBN: 3030041611

Category: Mathematics

Page: 691

View: 4356

MATRIX is Australia's international and residential mathematical research institute. It facilitates new collaborations and mathematical advances through intensive residential research programs, each 1-4 weeks in duration. This book is a scientific record of the eight programs held at MATRIX in its second year, 2017: - Hypergeometric Motives and Calabi-Yau Differential Equations - Computational Inverse Problems - Integrability in Low-Dimensional Quantum Systems - Elliptic Partial Differential Equations of Second Order: Celebrating 40 Years of Gilbarg and Trudinger's Book - Combinatorics, Statistical Mechanics, and Conformal Field Theory - Mathematics of Risk - Tutte Centenary Retreat - Geometric R-Matrices: from Geometry to Probability The articles are grouped into peer-reviewed contributions and other contributions. The peer-reviewed articles present original results or reviews on a topic related to the MATRIX program; the remaining contributions are predominantly lecture notes or short articles based on talks or activities at MATRIX.

Handbook of Mixture Analysis

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Author: Sylvia Fruhwirth-Schnatter,Gilles Celeux,Christian P. Robert

Publisher: CRC Press

ISBN: 0429508867

Category: Computers

Page: 498

View: 8336

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Predictive Statistics

Analysis and Inference beyond Models

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Author: Bertrand S. Clarke,Jennifer L. Clarke

Publisher: Cambridge University Press

ISBN: 1107028280

Category: Business & Economics

Page: 652

View: 6871

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

Computational Bayesian Statistics

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Author: M. Antónia Amaral Turkman,Carlos Daniel Paulino,Peter Müller

Publisher: Cambridge University Press

ISBN: 1108481035

Category: Business & Economics

Page: 275

View: 1915

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

Handbook of Parametric and Nonparametric Statistical Procedures

Fourth Edition

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Author: David Sheskin

Publisher: Chapman and Hall/CRC

ISBN: N.A

Category: Mathematics

Page: 1736

View: 9684

The fourth edition of the popular Handbook of Parametric and Nonparametric Statistical Procedures covers 160 of the most commonly used parametric and nonparametric statistical procedures encountered in theory and in practice. Featuring 26 new statistical tests and examples as well as 55 new figures, this edition provides new material on regression diagnostics, including the Durbin-Watson test. The book provides a new section on multivariate analysis with additional information on matrix algebra, uses of Hotellings T2, and multivariate analysis of variance and covariance. Sections on medical statistics, clinical trials, survival analysis, and analysis of censored data are also presented in this edition.

Bayesian Time Series Models

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Author: David Barber,A. Taylan Cemgil,Silvia Chiappa

Publisher: Cambridge University Press

ISBN: 0521196760

Category: Computers

Page: 417

View: 3607

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.