Name: multivariate bonferroni-type inequalities: theory and applications (en Inglés)
Price: 116150.00 CLP
Availability: InStock
Author: Chen, John
ISBN: 9781466518438

portada multivariate bonferroni-type inequalities: theory and applications (en Inglés)

Formato

Libro Físico

Autor

John Chen

Editorial

CRC Press

Año

2013

Idioma

Inglés

N° páginas

302

Encuadernación

Tapa Dura

Dimensiones

23.4 x 15.7 x 2.0 cm

Peso

0.54 kg.

ISBN

146651843x

ISBN13

9781466518438

Categorías

Inferencia bayesiana

multivariate bonferroni-type inequalities: theory and applications (en Inglés)

John Chen (Autor) · CRC Press · Tapa Dura

Libro Físico

$ 116.150

$ 232.300

Ahorras: $ 116.150

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Origen: Reino Unido (Costos de importación incluídos en el precio)

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Reseña del libro "multivariate bonferroni-type inequalities: theory and applications (en Inglés)"

Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamilton circuits. The first half of the book describes basic concepts and methods in probability inequalities. The author introduces the classification of univariate and multivariate bounds with optimality, discusses multivariate bounds using indicator functions, and explores linear programming for bivariate upper and lower bounds. The second half addresses bounding results and applications of multivariate Bonferroni-type inequalities. The book shows how to construct new multiple testing procedures with probability upper bounds and goes beyond bivariate upper bounds by considering vectorized upper and hybrid bounds. It presents an optimization algorithm for bivariate and multivariate lower bounds and covers vectorized high-dimensional lower bounds with refinements, such as Hamilton-type circuits and sub-Markovian events. The book concludes with applications of probability inequalities in molecular cancer therapy, big data analysis, and more.