Download or read book Random Fields written by Erik Vanmarcke and published by MIT Press (MA). This book was released on 1983-02-01 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to bring together existing and new methodologies of random field theory and indicate how they can be applied to these diverse areas where a "deterministic treatment is inefficient and conventional statistics insufficient."
Download or read book Multivariate Gaussian Random Fields written by Yuzhen Zhou and published by . This book was released on 2015 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Geometry of Random Fields written by Robert J. Adler and published by SIAM. This book was released on 2010-01-28 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important treatment of the geometric properties of sets generated by random fields, including a comprehensive treatment of the mathematical basics of random fields in general. It is a standard reference for all researchers with an interest in random fields, whether they be theoreticians or come from applied areas.
Download or read book Gaussian Markov Random Fields written by Havard Rue and published by Chapman and Hall/CRC. This book was released on 2005-02-18 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studies and, online, a 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 science fields where spatial data analysis is important.
Download or read book Stationary Sequences and Random Fields written by Murray Rosenblatt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has a dual purpose. One of these is to present material which selec tively will be appropriate for a quarter or semester course in time series analysis and which will cover both the finite parameter and spectral approach. The second object is the presentation of topics of current research interest and some open questions. I mention these now. In particular, there is a discussion in Chapter III of the types of limit theorems that will imply asymptotic nor mality for covariance estimates and smoothings of the periodogram. This dis cussion allows one to get results on the asymptotic distribution of finite para meter estimates that are broader than those usually given in the literature in Chapter IV. A derivation of the asymptotic distribution for spectral (second order) estimates is given under an assumption of strong mixing in Chapter V. A discussion of higher order cumulant spectra and their large sample properties under appropriate moment conditions follows in Chapter VI. Probability density, conditional probability density and regression estimates are considered in Chapter VII under conditions of short range dependence. Chapter VIII deals with a number of topics. At first estimates for the structure function of a large class of non-Gaussian linear processes are constructed. One can determine much more about this structure or transfer function in the non-Gaussian case than one can for Gaussian processes. In particular, one can determine almost all the phase information.
Download or read book Lectures On Probability And Second Order Random Fields written by Maria Felicitas Castanos and published by World Scientific. This book was released on 1995-05-31 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of lecture notes contains theoretical background material required for computer generation of random fields, which is of interest in various fields of applied mathematics.The necessary probabilistic background suitable for applied work in engineering as well as signal and image processing is also covered.The book is a valuable guide for higher level engineering students.
Download or read book Excursion Sets of Random Fields and Its Applications written by Florian Timmermann and published by GRIN Verlag. This book was released on 2011-06 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2011 in the subject Mathematics - Stochastics, grade: 1,0, University of Ulm, language: English, abstract: This work combines two beautiful branches of mathematics: geometry and random fields. The mathematical basics needed to understand the theory are developed carefully. Enriched with illustrative examples an easily implementable method for the analysis of a wide range of surfaces, e.g. paper or metallic surfaces, is provided and therefore suits for direct application.
Download or read book Gaussian Random Fields Proceedings Of The Third Nagayo Levy Seminar written by Kazufumi Ito and published by World Scientific. This book was released on 1991-11-29 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings emphasize new mathematical problems discussed in line with white noise analysis. Many papers deal with mathematical questions arising from actual phenomena. Various applications to stochastic differential equations, quantum field theory, functional integration such as Feynman integrals, limit theorems in probability are also discussed.
Download or read book Gaussian Random Functions written by M.A. Lifshits and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is well known that the normal distribution is the most pleasant, one can even say, an exemplary object in the probability theory. It combines almost all conceivable nice properties that a distribution may ever have: symmetry, stability, indecomposability, a regular tail behavior, etc. Gaussian measures (the distributions of Gaussian random functions), as infinite-dimensional analogues of tht
Download or read book K differenced Vector Random Fields written by Rehab Alsultan and published by . This book was released on 2015 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a great demand for analyzing multivariate measurements observed across space and over time, due to an increasing wealth of multivariate spatial, temporal, or spatio-temporal data, which may be treated as the realizations of vector (multivariate) random fields. As one of the most important random fields in theory and application, Gaussian random field has been extensively investigated in the literature. Non-Gaussian models and random fields are often encountered in many natural and applied science areas, with specific reasons for assuming particular non-Gaussian finite-dimensional distributions in practice. One of the objectives of this dissertation is to introduce a new non-Gaussian vector random field, which belongs to the family of elliptically contoured vector random fields. This new field is named the K-differenced vector random fields, because its finite-dimensional densities are the difference of two Bessel K functions. A K-differenced vector random field is of second-order and allows for any possible correlation structure, just as a Gaussian one does. It includes a Laplace vector random field as a limiting case. This dissertation studies the properties of the K-differenced vector random field and proposes some covariance matrix structures for not only a K-differenced vector random field but also a second-order elliptically contoured one. Other objectives of this dissertation are to construct the K-differenced random variable or random vector as the scale mixture of normal random variables or vectors and to derive its density and characteristic functions. Simulations of the K-differenced distribution have been made through Monte Carlo procedures. Maximum likelihood estimators of the parameters for the simulations are found numerically via MatLab.
Download or read book An Innovation Approach to Random Fields written by Takeyuki Hida and published by World Scientific. This book was released on 2004 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exploration of random fields. The authors use an approach that firstly constructs innovation, which is the most elemental stochastic process with a basic and simple way of dependence, and then they express the given field as a function of the innovation.
Download or read book Gaussian and Non Gaussian Linear Time Series and Random Fields written by Murray Rosenblatt and published by Springer Science & Business Media. This book was released on 2000 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The principal focus here is on autoregressive moving average models and analogous random fields, with probabilistic and statistical questions also being discussed. The book contrasts Gaussian models with noncausal or noninvertible (nonminimum phase) non-Gaussian models and deals with problems of prediction and estimation. New results for nonminimum phase non-Gaussian processes are exposited and open questions are noted. Intended as a text for gradutes in statistics, mathematics, engineering, the natural sciences and economics, the only recommendation is an initial background in probability theory and statistics. Notes on background, history and open problems are given at the end of the book.
Download or read book Multivariate Latent Gaussian Random Field Mixture Models written by and published by . This book was released on 2014 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Multiparameter Processes written by Davar Khoshnevisan and published by Springer Science & Business Media. This book was released on 2002-07-10 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-contained presentation: from elementary material to state-of-the-art research; Much of the theory in book-form for the first time; Connections are made between probability and other areas of mathematics, engineering and mathematical physics
Download or read book Bivariate Gaussian Random Fields Models Simulation and Inference written by Olga Moreva and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Innovation Approach To Random Fields An Application Of White Noise Theory written by Takeyuki Hida and published by World Scientific. This book was released on 2004-07-14 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: A random field is a mathematical model of evolutional fluctuating complex systems parametrized by a multi-dimensional manifold like a curve or a surface. As the parameter varies, the random field carries much information and hence it has complex stochastic structure.The authors of this book use an approach that is characteristic: namely, they first construct innovation, which is the most elemental stochastic process with a basic and simple way of dependence, and then express the given field as a function of the innovation. They therefore establish an infinite-dimensional stochastic calculus, in particular a stochastic variational calculus. The analysis of functions of the innovation is essentially infinite-dimensional. The authors use not only the theory of functional analysis, but also their new tools for the study.
Download or read book Markov Random Fields written by I︠U︡riĭ Anatolʹevich Rozanov and published by Springer. This book was released on 1982-11 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we study Markov random functions of several variables. What is traditionally meant by the Markov property for a random process (a random function of one time variable) is connected to the concept of the phase state of the process and refers to the independence of the behavior of the process in the future from its behavior in the past, given knowledge of its state at the present moment. Extension to a generalized random process immediately raises nontrivial questions about the definition of a suitable" phase state," so that given the state, future behavior does not depend on past behavior. Attempts to translate the Markov property to random functions of multi-dimensional "time," where the role of "past" and "future" are taken by arbitrary complementary regions in an appro priate multi-dimensional time domain have, until comparatively recently, been carried out only in the framework of isolated examples. How the Markov property should be formulated for generalized random functions of several variables is the principal question in this book. We think that it has been substantially answered by recent results establishing the Markov property for a whole collection of different classes of random functions. These results are interesting for their applications as well as for the theory. In establishing them, we found it useful to introduce a general probability model which we have called a random field. In this book we investigate random fields on continuous time domains. Contents CHAPTER 1 General Facts About Probability Distributions §1.
