Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Ce traité est le commencement d'un projet métaphysique nouveau. Il présente et examine l'idée qu'il y a un lien originaire entre l'espace et l'aléatoire. Informé tant de l'histoire de la métaphysique que de sa critique constante dans la tradition occidentale, il prend ainsi son départ dans une conception renouvelée et de la spatialité et de l'aléatoire. Concevoir qu'il n'y a pas un cadre ou principe global qui enveloppe et régit « tout », n'est que percevoir le verso, comme en négatif, d'une spatialité intrinsèquement multiforme. Dans cette optique, « l'espace » est donc la désignation d'une multiplicité de formes de spatialité. Corrélativement, une des thèses principales du traité est que ce qu'on appelle habituellement « le hasard » ou « l'aléatoire », ne manifeste en fait que les liaisons, discontinuités et incompatibilités qui naissent entre ces différentes formes.

Random fields.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics -a very active area of research in which few up-to-date reference works are available. Gaussian Markov Random Field: Theory and Applications is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. It includes extensive case studies and an online C-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of fields in which spatial data analysis is important.

Gaussian Markov random fields.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Random fields --- Spin glasses

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Stochastic processes --- Markov random fields.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Providing a novel approach to sparsity, this comprehensive book presents the theory of stochastic processes that are ruled by linear stochastic differential equations, and that admit a parsimonious representation in a matched wavelet-like basis. Two key themes are the statistical property of infinite divisibility, which leads to two distinct types of behaviour - Gaussian and sparse - and the structural link between linear stochastic processes and spline functions, which is exploited to simplify the mathematical analysis. The core of the book is devoted to investigating sparse processes, including a complete description of their transform-domain statistics. The final part develops practical signal-processing algorithms that are based on these models, with special emphasis on biomedical image reconstruction. This is an ideal reference for graduate students and researchers with an interest in signal/image processing, compressed sensing, approximation theory, machine learning, or statistics.

Stochastic differential equations. --- Random fields. --- Gaussian processes.