Modeling and Estimation of Reciprocal Diffusion and Gauss-Markov Random Fields PDF Download
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Author: CALIFORNIA UNIV DAVIS.
Publisher:
ISBN:
Category :
Languages : en
Pages : 4
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Book Description
The basic goal of this research was to develop a theory of second order stochastic differential equations as a class of model for problems of filtering and estimation. This goal has been achieved for both continuous and discrete time linear-Gaussian reciprocal processes.
Author: CALIFORNIA UNIV DAVIS.
Publisher:
ISBN:
Category :
Languages : en
Pages : 4
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Book Description
The basic goal of this research was to develop a theory of second order stochastic differential equations as a class of model for problems of filtering and estimation. This goal has been achieved for both continuous and discrete time linear-Gaussian reciprocal processes.
Author: Havard Rue
Publisher: CRC Press
ISBN: 0203492021
Category : Mathematics
Languages : en
Pages : 280
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Book Description
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-studie
Author: Rama Chellappa
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 608
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Book Description
Introduces the theory and application of Markov random fields in image processing/computer vision. Modelling images through the local interaction of Markov models produces algorithms for use in texture analysis, image synthesis, restoration, segmentation and surface reconstruction.
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 814
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Author: R. Chellappa
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
In an earlier report a consistent estimation scheme was given for Gaussian Markov random field models. In this report we consider some statistical properties of the resulting estimate. Specifically, we derive an expression for the symptotic mean square error of the estimate for a general model and compare the efficiency of this estimate with the popular coding estimate for a simple first order isotropic model. (Author).
Author: Xavier Guyon
Publisher: Springer Science & Business Media
ISBN: 9780387944289
Category : Mathematics
Languages : en
Pages : 294
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Book Description
The theory of spatial models over lattices, or random fields as they are known, has developed significantly over recent years. This book provides a graduate-level introduction to the subject which assumes only a basic knowledge of probability and statistics, finite Markov chains, and the spectral theory of second-order processes. A particular strength of this book is its emphasis on examples - both to motivate the theory which is being developed, and to demonstrate the applications which range from statistical mechanics to image analysis and from statistics to stochastic algorithms.
Author: Y.A. Rozanov
Publisher: Springer Science & Business Media
ISBN: 1461381908
Category : Mathematics
Languages : en
Pages : 207
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Book Description
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 ยง1.
Author: Ruggero Frezza
Publisher:
ISBN:
Category :
Languages : en
Pages : 344
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Book Description
Author: Murray Rosenblatt
Publisher: Springer Science & Business Media
ISBN: 9780387989174
Category : Mathematics
Languages : en
Pages : 272
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 1836
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Book Description
