1 edition of Statistical Analysis of Random Fields found in the catalog.
Published
1989
by Springer Netherlands in Dordrecht
.
Written in
Edition Notes
| Statement | by A. V. Ivanov, N. N. Leonenko |
| Series | Mathematics and Its Applications (Soviet Series) -- 28, Mathematics and Its Applications (Soviet Series) -- 28 |
| Contributions | Leonenko, N. N. |
| Classifications | |
|---|---|
| LC Classifications | QA276-280 |
| The Physical Object | |
| Format | [electronic resource] / |
| Pagination | 1 online resource (x, 244 p.) |
| Number of Pages | 244 |
| ID Numbers | |
| Open Library | OL27089135M |
| ISBN 10 | 940107027X, 9400911831 |
| ISBN 10 | 9789401070270, 9789400911833 |
| OCLC/WorldCa | 851393814 |
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Elements of the Theory of Random Fields.- Basic concepts and notation.- Homogeneous and isotropic random fields.- Spectral properties of higher order moments of random fields.- Some properties of Statistical Analysis of Random Fields book uniform distribution.- Variances of integrals of random fields.- Weak dependence conditions for random fields.- A.
Statistical Analysis of Random Fields. Authors: Ivanov, A.A., Leonenko, Nikolai Free Preview. Buy this book eB68 *immediately available upon purchase as print book shipments may be delayed due to the COVID crisis.
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Ivanov Part of the Mathematics and Its Applications (Soviet Series) book series (MASS, volume 28) Log in to check access. Buy eBook. USD Buy eBook Estimator Variance calculus correlation linear regression statistical analysis. Authors and affiliations.
Statistical Analysis of Random Fields (Mathematics and its Applications) Hardcover – J by A.A. Ivanov (Author), Nicolai Leonenko (Author)Cited by: Elements of the Theory of Random Fields Basic concepts and notation Homogeneous and isotropic random fields Spectral properties of higher order moments of random fields Some properties of the uniform distribution Variances of integrals of random fields Weak dependence conditions for random fields A.
(). A review of: Random fields: Analysis and synthesis. Transport Theory and Statistical Physics: Vol. 12, No. 4, pp. Author: Kamal C. Chanda. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation.
The last three chapters then develop more advanced statistical ideas, focusing on interval estimation, characteristic functions, and correcting Cited by: Existing and new methodologies of random field theory are discussed in terms of their application to diverse areas in science and technology where a deterministic treatment is inefficient and conventional statistics are insufficient.
The extent and characteristics of the random field approach are outlined, the classical theory of multidimensional random processes is reviewed, and basic Cited by: The question motivating this study is whether and how knowledge of the SPDE may simplify the statistical analysis of the associated random field.
Read more PreprintAuthor: Michel Goulard. The book has a nice balance of statistical theory, methodology, and applications, with an emphasis on statistical methods. It contains many concrete examples that illustrate both theory and methods.
In illustrating the methods, real and interesting data examples are drawn from many disciplines such as agriculture, ecology, geology, epidemiology. This book will be useful for scientists, engineers and advanced graduate students who need to develop their own statistical tools for the analysis of their data.
Whilst this book may not provide the reader with the specific answer it will inspire them to rethink their problem in the context of random fields, apply the method, and produce a. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and.
This book provides an introduction to the theory of random fields and its applications. It includes topics from classical statistics and random field theory, spatial statistics, and statistical physics.
It also explores links between random fields and Gaussian processes used in machine learning. Formal definition. Given a probability space (,), an X-valued random field is a collection of X-valued random variables indexed by elements in a topological space is, a random field F is a collection {: ∈}where each is an X-valued random variable.
Examples. In its discrete version, a random field is a list of random numbers whose indices are identified with a discrete set of points. Data Analysis Methods in Physical Oceanography is a practical reference guide to established and modern data analysis techniques in earth and ocean sciences.
This second and revised edition is even more comprehensive with numerous updates, and an additional appendix on. Using MATLAB ® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data.
The book focuses on methodological issues in analyzing structural brain. A numeric sequence is said to be statistically random when it contains no recognizable patterns or regularities; sequences such as the results of an ideal dice roll or the digits of π exhibit statistical randomness.
Statistical randomness does not necessarily imply "true" randomness, i.e., objective unpredictability. Pseudorandomness is sufficient for many uses, such as statistics, hence the.
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Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data.
This book is a comprehensive and illustrative. The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and.
The book includes a section with examples that show how to report the results of an analysis correctly. These examples can serve as templates for reporting an analysis, while avoiding the mistakes discussed in earlier chapters. The book’s author is the co-author of the text Introduction to Meta-Analysis, the best-selling text in this [email protected]{osti_, title = {Statistical Methods for Environmental Pollution Monitoring}, author = {Gilbert, Richard O.}, abstractNote = {The application of statistics to environmental pollution monitoring studies requires a knowledge of statistical analysis methods particularly well suited to pollution data.
This book fills that need by providing sampling plans, statistical tests.The theory of Markov random fields and -Markov fields has a number of important applications in quantum field theory and statistical physics (see,). Another class of random fields arising from problems of statistical physics is that of Gibbs random fields, whose probability distributions can be expressed as Gibbs distributions (cf.
Gibbs.
